Character and pattern recognition machine and method

ABSTRACT

Banking apparatus for high speed processing of bank checks, drafts and like documents having numeric characters in courtesy amount fields. The documents are conveyed along a path and the courtesy amount field with the numeric character are located, scanned, digitized and the numeric characters are supplied to a plurality of parallel connected artificial fovea, each artificial fovea including temporary storage and image section measurement circuits. Recognition scoring circuits identify the numeric characters from the respective artificial fovea.

This application is a division of application U.S. Ser. No. 07/244,425,filed Sep. 15, 1988, now U.S. Pat. No. 5,097,517, which was acontinuation in part of U.S. Ser. No. 07/026,817, filed Mar. 17, 1987,now abandoned, which, in turn, was a continuation in part of U.S.application Ser. No. 06/909,388, filed Sep. 19, 1986, now U.S. Pat. No.4,837,842.

BACKGROUND OF THE INVENTION

The present invention relates generally to character and patternrecognition machines and methods, and more particularly, to featureextraction systems for use with optical readers for reading characterswhich have been hand printed without any constraints, such as,surrounding box limits, red center lines, or similar artificial devices.One novel feature of this invention is in the method of choosing thefeatures and the highly normalized method of measuring the individualfeature parameters. The invention can be said to perform a crudesimulation of a little known psychological phenomenon occuring inprimates called the "saccadic flick".

The present invention also relates generally to bank check, drafts andthe like financial document processing machines and method incorporatingcharacter and pattern recognition systems and, more particularly, tosystems for reading numeric characters and symbols (e.g., "xx", fractionlines, etc.) and recognizing dollars and cents in the courtesy amountfield (CAF) of a bank check, draft and like business documents and whichhave been typed or printed, particularly hand printed without anyconstraints, such as surrounding box limits, red center lines, orsimilar artificial devices.

While there are generally different views on the definition of thefeatures of patterns, many studies made on the recognition of charactersas well as the recognition of patterns have proved that the so-calledquasi-topological features of a character or pattern such as theconcavity, loop, and connectivity are very important for therecognition. To date, many different methods have been proposed for thepurposes of extracting such quasi-phasic features. Up until thisinvention these methods all use analysis of the progressive slopes ofthe black pixels. Mori et al. U.S. Pat. No. 4,468,808 classifies thoseanalyses into three types. The first is the pattern contour trackingsystem developed by Grenias with IBM. Mori calls this a serial system.The second type is Mori's preferred, the earliest patented example ofwhich is Holt called the "Watchbird". In this type of analysissequential rows and columns are compared. Another example of thesequential rows and column type is Holt's Center Referrenced Using RedLine. Mori's third type is a parallel analysis system which Moridismisses as either taking too long or costing too much. All systemsinvolving the sequential analysis of the slope of black pixel groupssuffer severely from smoothing and line thinning errors. Worse yet, theyare very likely to produce substitution errors when the lines have voidsor when unwanted lines touch. A comprehensive survey of prior arthandprint recognition systems is found in an article by C. Y. Suen etal. entitled "Automatic Recognition of Handprinted Characters--The Stateof the Art", Proceedings of the IEEE, Vol. 68, No. 4, April 1980, whichis incorporated herein by reference. The preferred handprint characterrecognition technique of this invention uses none of the methodsmentioned by Suen et al. or Mori et al.

The character recognition system of the present invention, while usingquasi-topological features, employs a novel method of measuring andscoring such features, resulting in great improvement in performance ofthe reading machine.

Briefly, the character recognition system of this invention employsmeasurement of the enclosure characteristics of each white pixelindependently of other white pixels. Since the measurements are made intwo (or more) dimensions rather than in one dimension (such as slope),the results are insensitive to first order aberations such as accidentalvoids, touching lines and small numbers of black pixels carrying noiseonly. In the preferred embodiment, no noise processing is performed atall since all forms of noise processing are done at the expense ofaccuracy in recognition. As used herein, a pixel is defined as an imageinformation cell constituted by the binary states "on" and "off" or"black" and "white", respectively.

The financial document processing portion of this invention locates thecourtesy amount field (CAF) bank check and then locates the divisionbetween the dollars portion of the CAF and the cents portion and thenreads the dollar and cents amounts. Overlapping characters, overlappingand touching characters, symbols (e.g., "xx") "100" and characterstouching the fraction line, in the CAF and treated as a character unit.

SUMMARY OF THE INVENTION

This invention has the following desirable characteristics and features:

1) It recognizes typed or printed, especially handprinted numericcharacters and symbols ("xx", fraction lines, etc.), in the courtesyamount field (CAF) of a bank check, draft, for example, regardless oftheir absolute size, except when the size or relative size of theenclaves is necessary for discrimination between classes.

2) The invention recognizes numeric characters and symbols (e.g., "xx",fraction lines, etc.) in the CAF essentially independently of smallvariations in line thickness, independently of small voids, andindependently of small numbers of extraneous black pixels in and aroundthe character; this is accomplished because the recognition algorithm isbased on the normalized number of white cells within the enclave bounds,but not on the easily poisoned curvature of the black lines.

3) The invention is able to pick out well known numeric characters andsymbols from an image which includes a great many patterns of types notpreviously taught to the machine: this is accomplished because themeasurements of unknown enclaves must have high correlation withpreviously taught enclaves before they are even considered.

4) It is able to achieve a substitution rate (the percentage of wronglychosen class names divided by the number of class names correctly chosenby a human judge working from only the same images) of zero and thus itis particularly useful where bank check and similar documents are beingprocessed. This remarkable characteristic is accomplished because therecognition algorithm uniquely allows for continuous linear scoring andcomparison throughout all correlations. This characteristic is to bestringently observed as being specifically different from all forms ofdecision making in which choices are made on a yes/no basis; all formsof tree-based logical recognition methods have the inherent Achillesheel of making an absolutely wrong decision with absolute certainty.This is often caused by an insignificant variation in the image pattern.

5) Another great advantage of this invention is that it is able to judgethe acceptability of numeric characters and symbols on the basis ofcomparisons to an absolute Minimum Acceptable Level (MAL) and also to aMinimum Doubles Ratio (MDR). These virtues again spring from the linearscores which are continuously generated. These capabilities providegreat advantage because they allow the machine (or the operator) to varythe acceptability criteria depending on context, character set, qualityof images, etc. in the case of bank checks, for example.

6) Furthermore, the numeric character recognition system of thisinvention is adaptive in the sense that it can learn the names ofpreviously unknown patterns and automatically generate new enclaves andnew recognition equations which are carefully crafted to benon-conflicting with previously learned patterns. Typically the bank'smanual operator will provide a correct classification for a rejectedcharacter; non-supervised learning can also take place, e.g., learningusing an automatic dictionary.

7) With respect to handprinted numeric characters and symbols, one ofthe most important characteristics of my invention is its ability torecognize touching and overlapping characters. This has been, up untilnow, an impossible task for any handprint reader. This is accomplishedby two methods, which can be mutually supportive in their decisions. Thefirst is the use of "test segmentation and analysis". The second methodis by the use of "superclass" training, e.g., "three" touching a "four",for example is trained to be recognized as a new class called a"three-four". Likewise a blob in which a "one" touches a "zero" which inturn overlaps a "nine", for example, is recognized as the class',"one-zero-nine". In bank checks, the cents amount frequently is writtenas a fraction with arabic numerals denoting cents above a line or slash"/" and "100" indicating units or $1, e.g. XX/100. According to theinvention, the decimal point line or slash is located and the "100"below the line is ignored (in many instances the maker may simply notwrite out "100" e.g. "xx/".

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, advantages and features of the inventionwill become more apparent when considered with the accompanying drawingswherein:

FIG. 1A is a typical bank check which can be processed by the presentinvention, FIG. 1B is a typical deposit slip (reverse side), FIG. 1C isa typical adding machine tape having numeric characters and symbolswhich can be read according to the invention,

FIG. 2A is a block diagram of a bank check processing systemincorporating the invention, FIG. 2B is a block diagram of amodification of the bank check processing system shown in FIG. 2A, FIG.2C is a block diagram of the dollar, cents division locator of FIG. 2C,

FIG. 3 is a simplified block diagram of a pattern recognition machineincorporating the invention,

FIG. 4A is an example of a typical pattern stored in a Pattern Source,

FIG. 4B shows an exemplary output from an Artificial Fovea,

FIG. 5 is a Block Diagram of an Artificial Fovea,

FIG. 6 is a block diagram of a Recognition Scoring Device,

FIG. 7 is a Block Diagram of an enclave Measuring Device,

FIG. 8A is a diagram showing the Fovea Matrix, which contains specificcircuitry capable of performing most of the functions of an EnclaveMeasuring Device,

FIG. 8B is a circuit diagram showing a single Polyp In the NortheastQuadrant, a "Polyp" being defined as one element in the Fovea Matrix,

FIG. 8C is a Table showing how the names of the gate functions of FIG.8A are modified for Polyps appearing in other quadrants,

FIG. 8D is a Diagram showing a Single Typical Polyp In Each Quadrant,

FIG. 8E is a diagram of the Simplified Connections in a Fovea Matrix,

FIG. 9 illustrates Details of a Recognition Scoring Device,

FIG. 10A is a Table of Generic Enclave Names,

FIG. 10B illustrates five enclaves in the closed top numeral 4,

FIG. 10C is a table of the cardinal template scores for enclave No. 1 ofFIG. 3,

FIGS. 11A-11K comprise a Table of Arabic Numerals, conventionally drawn,showing positions of conventional generic enclaves,

FIG. 12 is a Table of Recognition equations, showing typical negationsand typical relative position requirements,

FIGS. 13A, 13A.1-13.4 illustrate Progressive Samples of MembershipQualification in a Generic Character,

FIGS. 13B, 13B.1-13B.3 illustrate Progressive Samples of MembershipQualification in a character with voids,

FIGS. 14A-14C illustrate Examples of Characters with Voids that wouldfail to be recognized by line tracking methods,

FIG. 14D illustrates the scoring pattern for FIG. 14A,

FIG. 14E illustrates the scoring pattern for FIG. 11B,

FIGS. 15A and 15B illustrate Examples of Characters with extra blackpixels that would fail to be recognized by line tracking methods,

FIGS. 16A-16E illustrate Examples of Other Characters which can berecognized, such as widely varying size,

FIGS. 17A-17K illustrate recognition of Touching and OverlappingCharacters in accordance with the invention,

FIG. 18 illustrates Artificial Fovea Shown as a Computer Element,

FIG. 19 illustrates Block Diagram of Parallel Pipeline Configurationshowing a plurality of Artificial Foveas in operation,

FIG. 20A is a block diagram of a partial recognition machine whichincludes a resolution modifying component,

FIG. 20B illustrates Examples of Resolution Considerations,

FIG. 20C illustrates reduction and quantization of FIG. 17B by 3X3,

FIG. 20D illustrates a quantization of FIG. 17B with no reduction,

FIG. 21A is a block diagram of a recognition system incorporating theinvention and utilizing a learning module,

FIG. 21B illustrates Learning Capabilities using a zero with an opening,

FIG. 22A and 22B illustrate Capabilities for Rejecting Nonsense Shapesand Disorderly Noise,

FIG. 23A and 23B illustrate the analysis of pointyness triangles forclosed top "4" and a well formed "9",

FIG. 24A, 24B and 24C, illustrate a "period" in black white, inverse,black white "period" and inverse display of a blob eight, respectively,

FIG. 25A is an example of a numeral requiring a relative size function,and

FIG. 25B is an example of a complex function derived from relativeenclave sizes,

FIG. 26A illustrates a typical fraction,

FIG. 26B illustrates an "xx" type symbol or zero value fraction,

FIGS. 27A and 27B are examples of machine formed characters read by theinvention,

FIG. 28 is a font from the standardized OCR-A,

FIG. 29 is a font of drawings from a less stylized OCR-B,

FIGS. 30A through 30E are examples of dot matrix machine printedcharacters read by this invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described in greater detail withreference to the illustrated embodiments.

FIG. 1 is a plan view of a conventional bank check or draft 310 havingthe usual areas for the printed name and address of the maker 311, date312, check number 313, which may be printed with magnetic ink in amachine readable format, "pay-to-the-order" of line 314, courtesy amountfield 315 wherein the amount of the check is entered by the maker inarabic numerals (e.g. numeric characters), the written or spelled amountline 316 on which the amount of the check is written or spelled inalphabetical characters, a "for" portion 317 which serves as a memo tothe maker, and a signature portion 318 for signature of the maker. Aline of machine readable magnetic ink character recognition (MICR)encoded characters 319 is provided which is preprinted on the check inmagnetic ink which show the bank and branch number, the "on-us"characters, and the maker's account number. In addition, the line ofMICR characters may also include the check number in machine readableformat on the same line as the other MICR characters. A blank space 320is provided for later entry of machine readable MICR characters of theamount which may be hand written or formed by machine typed in portions315 and 316, and which are to be read by the numeric character readingsystem disclosed earlier herein.

In current bank check/draft processing systems, large numbers of peopleare required to read the written or spelled amount field 316 and/or thecourtesy amount field 315 and operate a keyboard entry device forentering the machine readable MICR characters for printing in a machinereadable format along with the other MICR characters in the blank space320 along with the rest of the line of MICR characters 319.

FIG. 2A is a block diagram of an automatic check reading systemincorporated in a bank check processing system according to thisinvention. In the bank check processing system shown in FIG. 2A, anautomatic check feeder 330 feeds checks in a predetermined orientationfrom a hopper, magazine or other mechanical check storage system (notshown) to a scanner 331. Scanner 331 lifts off the courtesy amount field315 as well as any other field of interest which is desired to be read.A courtesy amount field locator 332 generates a high probability of thecoordinates of where to focus the reader's attention and may use thedollar sign or symbol ($) shown to the left of courtesy amountrecognition field 315, fluorescent background (as shown in the art),coordinates systems or a dimension system where the measurements of thebox or rectangle is taken and utilized to locate the particular field.It is to be understood that numeric characters in the CAF may extendoutside box 315 and still be read according to the invention. The CAFlocator causes the characters which are scanned and lifted off of thecourtesy amount recognition field 315 to be supplied to the recognitionsystem 333 and constitutes the pattern input of pattern source 10 ofFIG. 3. The handprint recognition system passes the features ortemplates extracted from the characters in the courtesy amount field 315to authentication unit 334 which compares these features or templateswith features or templates uniquely associated with the maker's accountand stored in a local memory 335. The local memory 335 stores featuresor templates associated with each maker's account number and it storesthis filtered information (which could be a joint or multiple partyaccount with features of all parties stored. It constitutes in effect atemplate histogram of features associated with the maker's account.Thus, the particular way that maker makes his zeros, ones, twos, threes,etc. . . . is stored in local memory 335 and associated with the maker'saccount number. Thus, the system automatically verifies the handwritingof the maker. Instead of being a local memory of features of templates(described more fully hereafter) associated with the maker'shandwriting, this local memory could just as well be a storage of thetype of font typewriter or other implement used for printing a check 310which is known to be located in the offices of the maker. Thus, ifdesired, the local memory or features or templates 335 provides aneasily adaptable system for validating each of the maker's checksautomatically. The advantage of this is that most counterfeiters orthose seeking to pass off checks of others will trace a signature fromanother check of the maker without sufficient due regard to the mannerin which the maker may write certain characters which have theirfeatures or templates stored in the local memory 335. Rejected checksare stored in reject hopper 370 for manual processing, and the machinelearning phase to establish the local memory 335, for example. If noneof the characters are rejected the information from the handprintrecognition system is supplied to buffer memory 336 which supplies theinformation to drive microprinter 339 which prints the courtesy amountin machine readable form in space provided in the lower right-handcorner below the signature line for the maker. At the same time, thebuffer memory 336 puts a header onto this data which is then supplied tothe bank's central computer check processor. In this embodiment, thecheck is then passed from the MICR printer 339 to the MICR reader 337which reads all of the MICR imprinted or all of the machine readablecharacters on the bottom of the check and feeds this information to thebank's central computer check processor 341 and also feeds thisinformation to the check sorter 338. The check is also transported bythe transport mechanism to the check sorter 338 where the check issorted into pockets and, if so equipped, into numerical order. Thecentral bank computer check processor 341 has a mass memory system whichcontains all of the data relating to all of the accounts of thatparticular bank and its customers. Each check therefor is processed bythe bank's central computer check processor 341 to debit checks orcredit deposits to the maker's account and produce a statement instatement generator 341 which is transported to envelope stuffer 342.

It will be appreciated that in some systems, the lift off of variousfields on a check may be done in such a way that a reconstructionthereof is printed out in the manner of a bank statement without need ofreturning cancelled the checks to the maker. Such a system thereforeliminates the need of returning the cancelled checks to the user. Eventhough the courtesy amount field is read by the handprint recognitionsystem 333, with an imprinting on the check of the courtesy amount fieldin machine readable characters, it is possible that there is no need toimprint that amount on the check since the reader of this inventiontakes the place and performs the function of the MICR characters for thecourtesy amount field. In many cases however, it is desirable to imprintthe MICR characters of the courtesy amount field in the space soprovided because the check typically will be sent on to other bankinginstitutions where the check will be machine processed withoutintervention of any human.

Thus, the present invention solves the problem of manually reading or"eye-balling" the bulk checks individually at the receiving or initialreceiving banking institution as is required by the Federal ReserveBanking System. The handprint recognition system 330 described earlierherein is capable of a less than human substitution rate which is highlyadvantageous since substitutional errors can be most damaging to thebank involved.

There is a vast variety of unconstrained writing of the dollar and centsamount in the courtesy amount field of a bank check.

In some cases the dollar amount shows a "6" and a "1" with a small tailat the base of the "1" and the cents amount may have two zeros "0", "0"over a line and two X's below the line to symbolize "100". In othercases the upper part of the "3" is not clear and indicates that agreater degree of resolution in the scanner is needed so this check maybe rejected. Similarly, when the CAF is unreadable and is thus rejected.Still in other cases, the zero in the "cents" portion is "open" in theupper left quadrant and would be rejected because of the uncertainty. Insome accounts two X's are placed above and below the line, respectively,to symbolize "zero" cents. In the learning phase, the local memory bank335 of memory features or templates associated with that particularaccount be trained to recognize this as a "zero cents" symbol for thatparticular account, along with other numeral writing peculiarities ofthe holder of that particular account. Recognition of the line is onecriteria for separating the dollar amount from the cents amount. Otherhand check writers use a decimal point, as do most typed or printedchecks. The decimal point is typically two characters from the right andafter the third character from the right, and it is important topositively recognize the decimal point and whether a check is above acertain amount e.g., above $10,000.00 or $1,000.00 etc. Some checkwriters do not use a comma to indicate "thousands". In most (but notall) cases, the space between the last character in the dollar portionof the CAF is larger than the spacing between characters and this canalso be used to segment the dollars from cents positions of the CAF.

In some cases a space is left between the dollar amount and the centsamount and the cents amount is written as two zeros connected by a line(a pseudo cursive technique used by some people). In such case, it ispreferable to initially reject the check and then train the apparatus torecognize that manner of writing the dollar and cents amounts for thatparticular account. Some automatic check writing machines use somesymbols to fill in a line before and after the dollar and cents amounts,such as a line of astericks (********) and the invention can be trainedto locate the astericks to help locate specific fields.

In a number of circumstances, where the separation between characters isnot clear or the check background introduces black pixels in thebackground, it is helpful to return to the analog image of thecharacters to possibly change some of the pixels from black to white andvice-versa and thus eliminate some ambiguous pixels.

Where there is a fractional character for the cents amount, theinvention recognizes the line (there can be more than one line) whichcan be horizontal or slanted (generally upwardly slanting from left toright and wipes out or ignores characters or symboles which are belowthe line. Moreover, by recognizing the presence of a fraction line, theneed to identify location of a decimal point is obviated, as theysignify essentially the same thing--namely, the cents amount of the CAF.

Referring now to FIG. 2B, check transport 330 transports batches ofchecks singly through the system where they are scanned and anelectrical black/white image produced which is supplied to CAF locator332 which utilizes objects in the image to locate the CAF 315. Forexample, the MICR characters are printed in magnetic ink and in a verystandardized font and hence are easily recognized and located. The CAFis located in a predetermined portion of the check 310. CAF locator 332may have additional inputs such as location of the bottom edge, checkdimensions, etc. to facilitate locating the courtesy amount field. TheCAF image is supplied to the dollars and cents division locator 360(shown in FIG. 2C). As discussed earlier herein, there are numerouscriteria useful for locating the division between dollars and cents,such as the fraction line, decimal point (which may be a comma whichwould be recognized using the same technique used to recognize a period(.) as a decimal point shown in FIGS. 24a AND 24B). The dollars andcents division locator essentially performs a segmentation functionbetween groups or strings of characters in the same field (CAF).Segmentation between characters is performed using the techniques shownin FIG. 17A, with the numbers of adjacent continuous path while pixelsdetermining the degree of segmentation and the ratio of such pathsbetween the last time character and the next character (or decimalpoint) may be uses as criteria in the dollar and cents division locator452 (FIG. 2C).

The division between dollars and cents locator 333 is shown in FIG. 2C.The dollar and cents division locator causes the respective dollarportion strings of characters to be delivered to character recognitiondollars circuits 334 and the cents strings of character and or symbolsto be delivered to the character recognition cents circuits 335.

In order to validate the "cents" portion of the CAF field, a doublereading can be performed by extracting any numeric cents amounts writtenon the written or spelled amount line 316 (FIG. 1A). A cents detector337 receives the digital electrical black/white image from scanner 331and similar to technique used to locate the dollar and cents divisionlocates the cents or fraction amount on spelled line 316. Of course, insome instances, the cents may also be spelled out (". . . and fortycents") in which case there would be no additional validation of thecents portion of the CAF. Any numeric characters located in apredetermined portion of the right hand portion of the written orspelled line 316 may be deemed the cents portion and supplied to acharacter recognition unit 338 the output of which is supplied to acomparator 339 and compared with the results of the characterrecognition of cents characters by recognition unit 334. When acharacter is unreadable or there is an uncertainty or ambiguity, asignal (labeled "reject" is produced which may result in the CAF amountnot being read. The dollars and cents amounts for the check batches arelisted and stored in lister 340.

The check batches may optionally further be validated by comparison withthe dollar and cents amounts on deposit slips and/or adding machinetapes. FIG. 16 shows a typical example of the reverse side of a depositslip 341 with the listed checks corresponding to the CAF. A typicaladding machine tape 342 is shown in FIG. 1C with the corresponding CAFamounts shown therein. The deposit slip 341 will usually have theaccount number and other data in MICR or machine reaable format, ondeposit slip 341 this would be on the reverse side. The deposit slipsare transported by scanner 344 to produce an electrical black/whiteimage and the numeric character strings 341-1, 341-2 . . . 341-n areread by deposit slip character reader 346 and listed and stored inlister 347 the output of which is compared in comparator 348 with thenumeric characters from check lister 340 to validate the reading of eachcheck's CAF amounts and since the numeric amounts in the CAF of eachcheck can be added to validate the amount of the deposit in the proof ofdeposit unit 349.

The numeric amounts in the CAF as well as the numeric amounts on depositticket 341 may be further validated by comparisons with an addingmachine tape 342. Adding machine tape 342 is conveyed by adding machinetape transport 350 through scanner 351 and the strings of printednumeric characters 342-1 . . . 342-n are read by reader 352, listed andstored in lister 353 and compared by comparator 348 against the list ofnumeric amounts read from the CAF of each check and the deposit ticketlistings. Comparator 348 may weight the reading according to thereliability of reading machine printed characters against handprintedcharacters whereby any disagreement between the adding machine tape readcharacters and handwritten versions, the adding machine tape readingwould prevail. Thus, if the adding machine tape reading agrees with thereading of the numeric amounts read from the CAF of the checks, butdisagrees with the reading from a handwritten deposit slip, for anygiven entry 341-1, 342-2 . . . 341-n, or 342-1, 342-2 . . . 342-n, theadding machine version would be deemed accurate and the deposit slipversion rejected.

The courtesy amount field locator 332 feeds a digital image of thenumeric characters and symbols in the courtesy amount field to afraction line detector 450, a decimal point detector 451, spacingdetector 452, "XX" symbol detector 453, size disconnector 454, andcursive connector detector 456. The fraction line detector 450 seeks thelongest generally horizontally extending line in the right side of theCAF (and a fraction line, if found, on the written amount line 316) orto the right of any spacing. It operates on black pixels (similar to thescheme for detecting overlapping but not touching characters (FIG. 17))by beginning with the left lower most black pixel and comparing it withits neighbor to the right and as long as there is found a contiguousblack pixel adjacent and to the right, or just above on a diagonal, thecomparison continues and if a predetermined number of pixels span agiven horizontal path which includes the slanted fraction line which, ofcourse, is more frequently used than a perfectly horizontal straightline. The verticality of the line, of course, has to be distinguishedfrom the numeral "one" ("1").

A decimal point is detected by detector 451 using the techniquesillustrated in FIGS. 24A and 24B. The spacing detector 452 utilizes thebrush fire principle shown in FIG. 17 to locate one or more adjacentcontinuous vertical sequence of white pixels between characters. The"XX" symbol detection 453 treats the XX symbol as a character to eidentified, and essentially as a dyad type (twin intertwined characters)in which given corresponding templates are stored in template storage15. Size descriminator 454 simply counts the number of vertical pixelsin the last two numeric characters (either as individual characters ofas a dyad) in the courtesy amount field averages them and does the samefor all of the characters to the left of the last two characters in thecourtesy amount field (which may be treated as a dyad or tryiad as shownin FIGS. 17A-K). If the average vertical black pixel count for the lasttwo characters is below the average black pixel count for all numericcharacters to the left of the last two characters is less than apredetermined value, it is assumed that the last two characters arecents characters. Finally, the cursive corrector detector 456 examinesthe upper portion only of the last two characters image to detect agenerally horizontal string of black pixels and the absence of anysequentially continuous vertically running line of white pixels, whichis thus deemed to supply the presence of a cursive line connector.

The logical outputs of each of the detector is supplied to AND gates450A, 451A, 452A, 453A, 454A and 456A which are gated by a read signalcombinations of these AND gate outputs may be selectively OR gated by ORgate 460 and its logical output selectively combined by a plurality ofAND gates 461, 462 and 463 with certain ones of detectors 451-456. Thusfor logical output on line 461-0 requires the presence of an output fromfraction line detector 450, spacing detector 452 and at least one of the"XX" symbol detector 453, dollars and cents size descriminator 454 orcursive character connection detector 456. A decimal point is notrequired in this case to locate the division between dollars and cents.This is merely exemplary of this aspect of the invention. In some cases,the check writer merely draws a line indicating cents, and such line canbe recognized as a fraction line, but if there are no characters abovethe line (e.g., the space is blank), it is assumed to be "00" cents. Theabsence of the cents indicia (fraction line, decimal point, spacing,etc.) is likewise deemed "00" cents.

Thus, the invention may utilize one of two fairly distinct methods ofreading a fraction image or frame. One of them is to simply wipe outeverything underneath a discovered fraction bar and then read, as asingle class, single entity, everything above the bar. In other words,what is being read is a double digit numeric sitting on a bar (thefraction line). The way this is done is simply call out a characterclass. If a "6 2" is sitting on a bar (62 cents) then according to theinvention, that is a separate character class just like a "1 0 9" is thetriac as disclosed in my above-identified application, this is aseparate character class. They are not the same set of characterequations as it would be for a touching "2", "3", for example. This ia a"2", "3" sitting on a bar and there may be several character equationsnecessary for that. So that is what we will call a dyatic bar.

Another totally separate way is to detect where the lowest of the bottomof the bar is and wipe out one row of black pixels from the bar thenelectronically cut the image remaining in half vertically so that if itimage is symmetrical then the "2" is on one separate frame and the "3"sitting on a bar on another separate frame. This can be read as a singlenumeral sitting on a bar. In other words, not much of an effort isactually made to segment the numeral from the bar itself except the factthat this invention will wipe out a piece of the bar and everythingunderneath it and then make two images out of it, left and right, andthen recognize them. If at that time, if it rejects, then the systemwill go back and wipe some more off and keep going until an acceptablecharacter one or the other and wipe some more off the other side if itdoesn't. If good numbers are not found then the system will go to thediatic sitting on the bar method. Again, the basic principle here is tohave great reliance and great capability to reject numeric image that donot have good scores.

Instead of a decimal point, a dash is sometimes used and then centsnumerals e.g. "2", "3". The dash is deemed the decimal point cents.Europeans also use commas instead of decimal points. Commas are a littlebit bigger than decimal points and the Europeans are pretty good thatway but they also, of course, use commas after the thousands. Thus, insome cases, recognition of a period can include recognition of a comma(,). FIG. 3 is a simplified block diagram of a pattern recognitionmachine which incorporates this invention. The arabic patterns may befrom widely different kinds of sources. Such objects may be handprintedarabic numerals, or machine printed characters. It can also be taught torecognize cursive script for reading the written dollar line.

The Pattern Source 10 shown in FIG. 3 therefore represents a source ofdata containing a pattern to be recognized which in this case is thedigitized image of the CAF of the bank check shown in FIG. 1. Since thisinvention is not concerned with methods of scanning original bank checkpatterns, the hardware per se of the pattern source in this embodimentcan be some form of well known type of computer memory, such as RandomAccess Memory (RAM). Pattern source 10 can also be part of an on-linesystem such as a display tube or panels or graphic tablet wherein eachresolvable element can be deemed a pixel and the movement of the stylus(or finger) on or over the tube, panel or tablet forms the character tobe recognized to thereby enter an private secure account number foraccess to automatic teller machines and the like, for validation of theuser by the user's writing style.

Block 11 shown in FIG. 3 is labeled Artificial Fovea. This blockrepresents apparatus which emulates the behaviour of a human fovea,including temporary storage of the image, shifting of the image,measuring of image sections (called "enclaves"), and scoring of thesemeasurements against earlier stored measurements (called "templates").The best of these scores are called "features".

Block 12 shown in FIG. 3 is labeled Recognition Scoring. Block 12represents apparatus which stores and evaluates a number of equations,called "Recognition Equations". These equations call for various featurevalues to be combined in such a way that the highest scoring equationreveals the most probable class to which the unknown image belongs.

This utilization device 13 represents a device which is an "end user" ofthe recognition process which, in this embodiment is the bank checkprocessing system.

FIG. 4A is an example of a typical pattern stored in a Pattern Source.Note that it has been shown in a two dimensional array and that thevalues of the pixels shown are binary, i.e., black and white. The twodimensionality and binary values are normal for most of the applicationsof this invention, although it is not restricted to thosecharacteristics. The number of pixels per pattern is not to be limitedto the number shown in this example; one of the great advantages of thisenbodiment of the invention is that the absolute size of the patterns isnormalized as an inherent part of the measurements. The pattern shown iscalled a generic handprinted `two` because the shape satisfies the basicrequirements for a handprinted "two" without including any other extrainformation. Such extra information (normally confusing to analysis)consist of extra loops (at the beginning or middle of the "two"), whitepixels in sections of black line pixels (voids), and extra non-connectedblack pixels and variations in the width of the line forming thecharacter. In most of the examples herein, the "line" width is shown asa single pixel but it will be appreciated that in many characters thiswill depend on the width of the writing implement.

FIG. 4B shows an exemplary output from an Artificial Fovea. Note thatthe Artificial Fovea 11 has found two excellent Centers of Recognition(CORs) and has labeled them "A" and "B". It has also Labeled all thepixels belonging to Enclave No. 1 with a numeric "1". Similarly thepixels in the second enclave have been labeled with a "2". TheArtificial Fovea 11 has also scored both enclaves against knowntemplates stored in its memory. It has found that a Feature called"Best-in-West" has the best score for Enclave No. 1, and that score is100. Similary the Artificial Fovea 11 has found that the best score forEnclave No. 2 is developed by a feature called "Best-In-East", whichalso has a value of 100. Score values run between zero and 100, andenclave with a strange shape, or a pattern containing a void would havelower scores.

FIG. 5 is a Block Diagram of an Artificial Fovea. It includes fourblocks. They are an Enclave Measuring Device 14, Template Storage Device15, Comparator 16, and Best Template Sorting Device 17.

The term "enclave", as used in this invention, means an area of whitepixels which are more or less bounded by black pixels or the edge of theimage. An example of an enclave is an area of white pixels surrounded byblack pixel another example is an area of white pixels bounded by blackpixels except in one or more directions. Some useful enclave shapes areshown in FIG. 7.

It is important to emphasize at his juncture that almost any white areacan be defined as an enclave, and that the most useful enclaves arememorized by humans and by this apparatus of my invention.

Referring again to FIG. 5, Enclave Measuring Device 14 is shown in muchmore detail in FIG. 7. Stated simply, enclave measuring device 14produces a set of measurements which describe the shape of the areawithin the enclave. These measurements emphasize the differences betweenenclaves that are necessary to separate pattern classes from each other,but they "normalize" the differences between enclaves which are notnecessary for separation of classes. These measurements primarilydescribe the positions and abundance of those white pixels which arebounded by black pixels on the edges of the enclave. In one embodimentthe area of the enclave is divided into quadrants, designated NorthEast(NE), SouthEast (SE), South(West (SW), and NorthWest (NW). In eachquadrant there are four possible pixel types: those that are not bounded(within that quadrant) by black pixels in either the vertical orhorizontal direction, those those that are bounded vertically but nothorizontally, those that are bounded horizontally but not vertically,and those that are bounded both horizontally and vertically.

Template Storage Device (TSD) 15. stores hundreds of selected sets ofmeasurements for comparison at a later time with measurements taken fromnew and unknown enclaves. As soon as these sets are selected and storedthey are known as "Templates". The physical methods used to store theTemplates can be any type of memory that has reasonable access time suchas RAM, ROM, magnetic disks, optical disks, etc. If the memory isdynamic or volatile, procedures must be provided to maintain theinformation or to reload.

Comparator 16 correlates the output of the enclave measuring device 14with each one of the hundreds of Templates stored in the TSD 15. Theresult of each correlation is a score running linearly between zero and100.

One embodiment of Comparator 16 develops its score by considering eachquadrant independently; the absolute differences between the enclavemeasurements and the Template values are summed and normalized. Thehardware used in the comparator may consist of an absolute valuesubtraction circuit, plus a summing mechanism and a low accuracydividing circuit to take percentages.

Best Template Sorting Device (BTSD) 17 accepts each new score producedby Comparator 16 and stores the value of that score in a list which hasbeen ordered by the value of the score. The identifying number of theTemplate is to be stored in such a way that it can be identified asbelonging to each score, and the coordinates of the Center ofRecognition (COR) used by the EMID 14 must likewise be associated withthe score. In practice, only the scores associated with the best twoTemplates must be kept by the BTSD 17. When an end to the particularscoring cycle has occurred, the BTSD 17 will output the Best TemplateNumber, the Best Template Score, and the coordinates of the Test CORwhich defines the winning enclave.

The concept of orderly rejection of nonsense shapes and disorderly noiseis a strong feature of this invention. An important output of the BTSD17 is the REJECT capability. In order to make intelligent rejection oftest enclaves two inputs must be additionally provided to the BTSD 17;these are the Minimum Acceptable Template Level (MATL) on line 18, andthe Minimum Template Doubles Ratio (MTDR) on line 19. The object ofthese inputs is to force the output of the BTSD to be a reject if eithera) the absolute score of the best template is less than the MATL, or b)the ratio of the best score to the next best score (of another template)is less than the MTDR.

FIG. 6 shows a block diagram of a Recognition Scoring Device. Itspurpose is to perform the second and final stage of recognition ofindividual patterns. It does this by using the feature scores toevaluate the character equations, sorting the resulting equation scores,and performing acceptability testing.

With reference to FIG. 6, Feature Storage Unit 20 stores the informationprovided by the operations of the artificial fovea shown in FIG. 5. Thisinformation consists of a set of data describing the best correlation orthe best template number on line 21-A obtained for each of a pluralityof enclaves within the image; the set also includes the Best TemplateScore inputted on line 21-B for each of the winning scores and a shortdescription of the location of each enclave, in the form of the CORcoordinates on line 23, with respect to the other enclaves. Physicallythe Feature Storage 20 comprises a digital memory which has an accesstime reasonably matching the speed requirements of the system.

Equation Storage Unit 24 stores recognition equations which have beendeveloped by previous human analysis and machine experience. Accordingto the invention. These recognition equations typically are the sums ofterms. Each term consists of a sign, a weighting factor (w), and thevalue of a particular feature. The memory or storage hardware hardwareperforming Equation Storage Unit 24 is similar to the hardware chosenfor the Feature Storage Unit 20.

Equation Evaluation Device 26 performs the additions, subtractions,multiplications, and divisions which are called out by the recognitionequations stored in the equation Storage 24. It must also perform anylogical operations called out by the equations, such as relativelocation requirements. Physically, the Equation Evaluation Device 26 ispreferrably a set of dedicated hardware chips which perform high speedarithmetic and logical functions. It may also consist of a fairlygeneral purpose computer chip.

The Best Score Sorting Device 27 and the Acceptability Testing Device 28are almost exactly similar in function to the Best Template SortingDevice 17 shown in FIG. 5. Its output consists of the name of arecognized character if the acceptability criteria (minimum acceptablecharacter level and minimum character doubles ratio) are passed; if thecriteria are not passed, a REJECT code is produced.

FIG. 7 is a Block Diagram of an Enclave Measuring Device 14 shown inFIG. 2. Storing and Shifting (S&S) device 30 accepts a pattern or imagefrom the Pattern Source 10 shown in FIG. 3. This pattern may betransferred from Pattern Source 10 either by any one of several parallelinformation transfers or serially, pixel by pixel. Note that at thispoint in the processing the pixels have only two states or "colors":black and white, "on" or "off", "1" or "0". Because the pattern will beshifted often during the operation of the Artificial Fovea it will beconvenient to have the pattern loaded into the S&S Unit 30 using aserial method. The S&S Unit 30 provides information directly to almostall of the other blocks in FIG. 7. The pattern is initially shifted sothat a white pixel falls right on the center of the storage area of theS&S Unit 30. This center of the storage area is abbreviated CSA.

Element 31 is a Boundedness Determining Unit. The meaning of"boundedness" in this invention is that each white pixel is called"bounded" if any black pixel exists in the same row or column at adistance further away from the Center of the Storage Area (CSA) than thelocation of the white pixel. A pixel may be bounded vertically only,horizontally only, or bounded both vertically and horizontally. It willbe appreciated that the states may be inverted wherein a white pixelbecomes a black pixel and a black pixel becomes a white pixel.

Pixel Type Determining and Type Counting Unit 32 performs the functionsof labeling each of the pixels with labels describing their boundednesscharacteristics, and then counting the absolute number of pixels of eachdifferent type. For nomenclature purposes, the area around the CSA isdivided into quadrants named NorthEast, SouthEast, SouthWest, andNorthWest (see FIG. 8A). There are four types of pixel in each quadrant,so the total number of descriptors per enclave is 4 types time 4quadrants, making 16 descriptors.

Enclave Membership Qualifying Unit 33 specifies, according to specificrules, which of the white pixels surrounding the CSA are to be includedas belonging to an enclave. This block performs this qualificationprimarily by using information obtained from the Boundedness Determiningoperation of unit 31.

Percentage of Pixel Type Determining Unit 34 performs a simple lowaccuracy (1%) division procedure in which the absolute number of eachpixel type in each quadrant is multiplied by 100 and divided by thenumber of enclave members in that quadrant. These percentages are, infact, the enclave measurements.

Finally, Filling of Best Enclave Unit 36 performs a function whichoccurs after final determination of which enclave best matches a storedtemplate, as described in FIG. 5. This operation shifts in codes to thestorage matrix 30 which are stored along with the color (black or white)of the pixel. These codes will prevent each pixel thus coded frombecoming a member of another enclave.

FIG. 8A is illustrates a Fovea Matrix. It shows a preferred embodimentof most of the functions of an Artificial Fovea. FIG. 8B, FIG. 8C, FIG.8D, and FIG. 8E contain additional details of the embodiment.

The Fovea Matrix 40 shown in FIG. 8A is a 13 by 13 square array ofelements called "Polyps". The exact number of Polyps 41 may vary fromapplication to application and is only critical in the sense that thenumber of polyps be greater than any enclave which is necessary to bemeasured in a given application. The odd number of polyps on each edgeis significant only because an odd number yields a central symmetryabout both the vertical and horizontal axes. The system chosen to numberthe Polyps is one which labels all quadrants symmetrically, except forminus signs. Thus, the central Polyp is labeled P00, the furthest NEPolyp is labeled P66, the furthest SE Polyp is labeled P6,-6; thefurthest SW Polyp is labeled P-6, -6; and the furthest NW Polyp islabeled P-6,6.

FIG. 8B is called "Polyp in NE Quadrant". This figure illustrates actuallogical electronic circuitry which will perform many of the complexfunctions required of an Artificial Fovea. This figure describes thecircuitry that every Polyp in the NE Quadrant will contain. (With somesign changes (shown in FIG. 8C), this circuitry will also apply to thePolyps of all other quadrants.) The numbering of the Polyps is importantto the understanding of the operations. The generalized NE Polyp of FIG.8D is labeled Pi, j; the subscript "i" stands for the number of thevertical column of which the Polyp is a member, and the subscript "j" isthe number of the horizontal row. This numbering system is consistentwith the Pi,j elements shown in FIG. 8A.

FIG. 8B contains 5 groups of circuits which are closely related to theblocks shown in FIG. 7. The first group is labeled "Polyp Imageregister" 43, and its function is to perform the storage and shiftingfunctions (S&S) of the Fovea Matrix described earlier. The second andthird groups perform the "Boundedness Determining" described inconnection with FIG. 7. The fourth group performs the qualifications ofenclave membership for that Polyp and also stores the membership status.The fifth group (called the "Fill Register") stores a binary one if thatPolyp has been previously selected as part of an enclave.

The Polyp Image Register (PIRi,j) 43 performs the functions of bothstorage of the pixel color and that of a shift register. This type ofelement is well known in the art, being basically a flip-flop with ashifting gate 63 having enough dynamic storage characteristics to allowit to act also as one stage of a shift register. It receives its colorinput from PIR[i-1] [j], which is located on the same row directly tothe left; its shifting output goes to PIR[i] [j], which is located onthe same row directly to the right. Polyps on the left edge of a rowreceive their inputs from the rightmost Polyp in the next lowest row,while Polyps on the right edge of a row shift their outputs to theleftmost element in the next highest row. This is illustrated in FIG.8E.

Referring still to FIG. 8B, the Vertical Closure Register 44 (whoseoutput is VCR[i] [j]) becomes a binary "one" if any of the Polypsfurther up in the vertical column contain a black pixel description.This is accomplished by using the "OR" gate 64 whose inputs are labeled31 and 32. Input 31 is true if the Vertical Closure Register 44 of thePolyp immediately above Pij is true, and this sets VCRij to a truestatus. Input 32 is true if the Polyp immediately above Pij is storing ablack pixel color; if true, it also sets VCRij to true status. Thismatrix arrangement of OR gates provides a "ripple" such that within asmall portion of a microsecond the presence of a black pixel at the topof any matrix column will cause the VCRs 44 below it to propagate a"true" status downward. The Horizontal Closure Register VCRij 45 has asimilar logical set of gates 65, and its function is the same except forthe fact that it detects boundedness to the right of Polyp Pij.

The Enclave Membership Register 46 of FIG. 8B uses many of the outputsof surrounding Polyps to determine whether the the pixel represented byPij is qualified for enclave membership. Inputs 53 and 54 connect to ANDgate 55 which becomes true if the Polyp just to the left of Pij is amember and if Pij itself is bounded horizontally. Inputs 56 and 57connect to AND gate 58 which becomes true if the Polyp just under Pij isa member AND if Pij is itself bounded vertically, OR gate 59 becomestrue if either gate 55 or gate 58 becomes true, and this will cause theenclave membership Register 46 to be true unless there are any"inhibitions". Inhibitions are applied to the EM 46 via'OR gate 60; ifit is true, then the EMOR remains false. Gate 60 becomes true if any ofits inputs become true. Inhibiting inputs are as follows:

FLRij

PIRij

PIR[i-1] [j-1]

PIR[i-1] [j-2]

PIR[i-2] [j-1]

PIR[i-2] [j-2]

Fill Register FLRij 61 requires that the Polyp may not be a member of anew enclave if it has already been chosen as part of another enclave.PIRij requires that if the pixel represented is black, that Polyp maynot be a member of any enclave. The other four PIR inhibitions representthe inhibiting effect of black pixels at points closer to the Center ofthe Storage Area (CSA). The output of OR Gate 40 collects these signals,which are collectively called "Line-of-Sight Inhibitions". Theyeffectively prevent enclave membership from propagating in an area whichis mostly isolated from the main enclave. Other functions besides thefunction represented by OR Gate 40 may be used to accomplish thisresult. Note that such functions must not prevent legitimate propagationthrough "pepper noise" within an enclave.

The last of the circuitry groups in FIG. 8B is the Fill register 61 andits output is called FLRij. It is loaded through shifting gate 62 whoseinput is from the Fill Register directly to the left. The Fill signalsare supplied and shifted through the Fovea Matrix each time a BestEnclave has been selected. The shifting is accomplished by exactly thesame technique as that used for loading the pattern itself.

As mentioned before, FIG. 8A applies in detail only to those Polyps inthe NE quadrant. When modified by the information in FIG. 8D, however, adesign for all four quadrants can be obtained from FIG. 8A.Specifically, FIG. 8C is a table showing the gate input designations foreach of the different quadrants.

Using a Fovea Matrix nomenclature which has a zero column number and azero row number creates confusion as to which quadrant the zero columnand zero row should be assigned during enclave measurements. From atheoretical point of view it does not matter so long as the choice isconsistent. Furthermore, if the resolution of the data is sufficientlyhigh, the data of this row and this column can be discarded. From a costpoint of view, however, resolution must be kept as low as is possibleconsistent with good results. In this description the zero row to theeast of the CSA is considered part of the NE quadrant, the zero columnto the South of the CSA is considered part of the SE quadrant, the zerorow to the West is treated as part of the SW quadrant, and the zerocolumn to the North is treated as part of the NW quadrant.

FIG. 8D shows a Single Polyp in each quadrant. The main purpose of thisfigure is to show the additional circuitry which is used to calculatethe percentages used in the measurements. Of the many possible ways ofelectronically generating percentages I have chosen an analog method asthe preferred embodiment. In order to do this it is first necessary togenerate signals that are proportional to the absolute number of pixels.In the NW quadrant these signals are NWM (NorthWest Membership), NWH(NorthWest Horizontal), and NWV (NorthWest Vertical). They arerespectively EMR (Enclave Membership Register) through a high value ofresistance, HCR (Horizontal Closure Register) through a high value ofresistance, and VCR (Vertical Closure Register) through a high value ofresistance. All of the NWM points are to be tied together and alsoconnected to a very low impedance device shown in FIG. 8E as anoperational amplifier 70. The voltage output of the operationalamplifier will be proportional to the absolute number of enclave membersin the NW quadrant. The sum of NWH and NWV are similarly generated.

FIG. 8E also shows analog circuitry for generating %V and %H for the NWquadrant. The circuitry uses operational amplifiers 71V and 71H withappropriate inputs, as shown. Circuitry for generating similar signalsare to be provided for each of the quadrants.

FIG. 8E additionally shows the preferred method of shifting the patternPIR and fill FLR information through the Fovea Matrix.

FIG. 9 shows details of a recognition scoring device 12. This is anexpansion of FIG. 6, which discusses the functions from a block diagrampoint of view. The preferred embodiment of the Recognition ScoringDevice 12 is a serial computer of the classical Von Neuman type. Itincludes a Best Feature Storage device 60, a Central Processing Unit 61,an Equation and Control Storage Device 62, and an Output Storage device63.

When the Artificial Fovea of FIGS. 1, 2, and 5 has finished its work itoutputs the Best Features found in the pattern to the CPU 61, which inturn stores them in the Random Access Memory (RAM) called Best FeatureStorage 60. CPU 61 then proceeds to evaluate the Equations which arestored in Read-Only-Memory (ROM) 62. This all is done under the controlof the Program, which also resides in ROM 62. The sorting of theequation scores and the acceptability testing is also done under controlof the program in CPU 61. The name of the Accepted Class, plusinstructions about what to do if the character is rejected, are allstored in a section of RAM called Output Storage 63. The separation ofRAM shown in FIG. 9 is made only for illustrative purposes, and manyother assignments may be used. There is no specific reason, for example,why Equation and Control Storage 62 cannot also be RAM"; since theinformation stored in that memory does, in fact, change less often thanthe information stored in memories 60 and 63, the use of ROM" isindicated on grounds of higher performance at less cost. Although theuse of special purpose hardware designed specifically to perform therecognition scoring function is perfectly possible, the preferredembodiment is a general purpose computer because of the economiespossible. Its high degree of flexibility is also valuable. The onlydrawback to using a general purpose computer here is its speed, which isslow compared to dedicated hardware. If speed becomes a problem, it isquite easy to add more microprocessor computers in parallel to performthe Recognition Scoring function.

FIG. 10A is a Table of Generic Enclave Names. In the sketches shown inthis table, the black pixels are represented by 'X marks, while thewhite pixels are represented by "dot" or "period" marks.

The Table shows four different major classifications of names ofenclaves. The first classification is that of "Loop". There are nosub-classifications. A score of 100 on the generic loop template wouldbe achieved by a symmetrical area of white pixels which is totallysurrounded by black pixels. This also implies that each of the quadrantsscored 100% of pixels being bounded in both the vertical and horizontalaxes. (It will be appreciated that the boundedness determinations can bemade on diagonals just as easily and the terms "north", "south", east",and "west" are merely terms of reference.) The numeral zero, if no voidsexist, will score 100 on the Loop generic template. Enclave No. 1 ofFIG. 10B will also score 100 on the Loop generic template, even thoughthe shape is triangular.

The next classification is called "Cardinal Concavities", and thesub-classes are called "Generic North", "Generic East", "Generic South",and "Generic West". In order to achieve a score of 100 on the GenericNorth template, an enclave must have 1) every white pixel in the NE bebounded to the East but not to the North, 2) every white pixel in the SEbe bounded to the East and the South 3) every white pixel in the SW bebounded to the South and the West, 4) every white pixel in the NW bebounded to the West but not bounded to the North. Any pixels deviatingfrom these specifications will cause the score of the enclave on thistemplate to be proportionately reduced.

In order to score 100 on the Generic East template, the white pixels inthe enclave must be bounded in all directions except the East. In otherwords, this generic template has the same requirements as the GenericNorth template except the requirements are rotated 90 degrees clockwise.

Similarly, the Generic South and Generic West templates haverequirements which are looking for white pixels that are bounded inthree directions but unbounded in the fourth direction.

Two examples of Cardinal Concavities are shown in FIG. 4B. Enclave No.1' will score 100 on the Generic West template, and Enclave No. 2 willscore 100 on the Generic East template.

The second major classification of generic templates shown in FIG. 10Aare called "Corner Concavities". Four sub-classes are illustrated. Theyare called "NE Square", "SE Square", "SW Square", and "NW Square". Threeexamples of good corner concavities are shown in FIG. 10B, which is amatrix sketch of a closed top numeral four. They are enclaves No. 2, No.3, and No. 4. No. 2 will score 100 on the NE Square template, No. 3 willscore 100 on the SE Square template, and No. 4 will score 100 on the SESquare generic template.

The third major classification of generic templates are called cornerconvexities. Four subclasses are called NE Vex", SE' Vex, "SW Vex", and"NW Vex". An illustration of an enclave which scores 100 on the NW Vextemplate is enclave No. 5 of FIG. 10A.

FIG. 10C is titled "Cardinal Template Scores for Enclave No. 1 of FIG.4B". In this table, the various templates are given numbers of exemplarypurposes. The Generic North template is given the number T10; T11 is aspecific template with major opening to the north. All templatesnumbered T10 through T19 are north templates. Similarly, T20 is theGeneric East template, with T21 being a template for some specificunusual east opening enclave. Again the numbers T20 through T29 arereserved for east opening template. South and West templates aresimilarly numbered.

The scores shown in FIG. 10C are the scores that the measurementsobtained from enclave No. 1 would have attained on T10, T11, T20, T21,T30, T31, T40, and T4I. The name "Best-in-North" is given to the bestscore of T10 through T19. The name "Best-in-East is given to the bestscore of the templates numbered 20 through 29. The names Best-in-Southand Best-in-West are similarly derived. These inclusive titles forscores derived from general characteristics of templates is a powerfultool. Particularly this method reduces the number of Recognitionequations by a large factor. The size of this factor is applicationdependent, but is at least equal to 10 or better.

If the scores for Enclave No. 2 of FIG. 4A were computed, they wouldshow that T20 had obtained a score of 100, and that the Best-in-Eastscore was 100. Other scores will all be 75 or less.

FIG. 11A through FIG. 11K comprise a table of eleven Arabic numeralswhich can be described using generic enclaves. The Recognition Equations(REq) shown contain only the Assertion terms and the Relative PositionRatio terms. They do not include Negation terms, which will be discussedin a another section.

FIG. 11A shows a loop as being the best enclave, and a generic zero isdefined as being the score of the Best Loop template.

FIG. 11B shows a single stroke ONE, sloped left. The score of thisgeneric shape is the score of the best NW VEX plus the score of the BestSE VEX, the sum divided by two. A right sloped ONE is similar but usesNE VEX and SW VEX.

FIG. 11C shows the most common shape of a handprinted TWO. It has noextra loops. The simplest form of the Recognition Equation is equal to(BIW+BIE)/2 times the Relative Position Ratio Two (RPR[2]). RPR[2] is afunction which is equal to unity if all the BIW enclave pixels are aboveor to the left of the BIE pixels. RPR[2] goes to zero very quickly if alarge percentage of enclave pixels violate this requirement.

FIG. 11D shows a generic THREE. It has two major enclaves. These arelabeled Best In West (BIE) and Second Best In West (SBIW). ItsRecognition Equation is equal to (BIE+SBIE)/2.

FIG. 11 E shows an open top FOUR. Its important enclaves are Best InNorth (BIN), Best SW SQ, Best NE SQ, and Best SE SQ. Its RecognitionEquation is equal to the sum of these scores divided by 4.

FIG. 11F shows a closed top FOUR. Its important enclaves are Best SharpLoop, Best SW SQ, Best NE SQ, Best SE SQ, and Best NW VEX. The SharpLoop function will be defined later in the specification. The"sharpness" function helps separate the closed top FOUR from a NINE.

FIG. 11G shows a generic FIVE. Its Recognition Equation is equal to(BIB+BIW)/2 times RPR[5]; where RPR[5] is the Relative Position Ratiofunction for the numeral FIVE.

FIG. 11H shows a generic SIX. Three acceptable enclaves are shown.Enclave No. 1 is accepted as "Best-In-East", Enclave No 2 as "Loop", andEnclave No. 3 as "Best NWVEX". Enclave No. 3 is only marginallyacceptable, since the number of pixels in it is a small percentage ofthe total number of pixels in the total white pixel image. Thisillustrates the point that marginal enclaves do not need to be specifiedwhen writing a Recognition Equation (REq) for generic characters. Thusthe REq for a generic SIX is equal to (BIE+Loop)/2 times RPR[6]; whereRPR[6] is the relative Position Ratio function for the numeral SIX.

FIG. 11I shows a generic SEVEN. Its acceptable enclaves are Best-In-Westand Best SEVEX. Its recognition equation is equal to (BIW+SEVEX)/2. Notethat none of the Recognition Equations discussed in connection with FIG.11A through 11K show any of the "negation" terms. For the SEVEN, one ofthe appropriate negation terms would be some fraction of Best-In-Eastscore; this would prevent the pattern shown in FIG. 11G from producing agood score on the REq for SEVEN.

FIG. 11J shows a generic EIGHT. Its acceptable Enclaves show a Best Loop(BL), a Second Dest Loop (SDL) and a Best-In West. Because double loopsappear coincidentally in many other handprinted numerals, the BIE termmust be used together with a Relative Position Ratio which is unity ifone loop is above the BIE which in turn is above the second loop. RPR[8]falls quickly to zero for numeral shapes which do not meet thisfunction. The recognition equation for EIGHT is equal to (BL+SBL+BIW)/3times RPR[8].

FIG. 11K shows a generic NINE. Its major enclaves are a Best Loop (BL),a Best-In-West (BIW) and a SEVEX. Although no other real numeral shouldhave a BIW over a BL, it is good practice to protect against garbagepatterns by adding the RPR[9] term which specifies that the BL must beover the BIW to be a high scoring NINE. The REq for NINE, (withoutnegation terms) is equal to (BL +BIW)/2 times RPR[9].

DISCUSSION OF RECOGNITION EQUATIONS

FIG. 12 shows an exemplary set of eleven Recognition Equations for theArabic numerals ZERO through NINE, including separate cases for the opentop FOUR and the closed top FOUR.

The nomenclature used is designed to facilitate the logical sorting andinclusion of generic templates which has already been discussed. Thus,REQ 0-0 means the Recognition Equation which represents the most commonshape of a ZERO. Its shape is shown in FIG. 11A. Similarly, REQ 1-0means the Recognition Equation for the most common shape of a ONE. Thefirst subscript is the numeral CLASS, while the second subscript beingthe specific shape type within the CLASS, REQ 4-0 is the genericequation for an "open top"FOUR, while REQ 4-1 is the generic equationfor a "closed top" FOUR. Note that the best scores from each class mustcompete against each other to exceed the MINIMUM ACCEPTABLE DOUBLESRATIO (MADR) (Refer to FIG. 6), but MADR does not apply within the sameCLASS name. Thus, a score of 90 for REQ 4-1 is acceptable over a scoreof 87 for REQ 4-0; the best CLASS FOUR score must have a Doubles Ratioof better than the Best Class score of every other class, however, In atypical case, the most likely class to be competing closely with theclosed top FOUR is CLASS NINE. Thus, using a typical MADR of 1.2, all ofthe other class scores must be less than 72 for a Best Class score of 90to be accepted.

Three types of terms are used in FIG. 12. They are the Assertion terms(such as BL, BIW, etc.), the Negation Terms (such as NEG[SBL], and theRelative Position Ratio terms (such as RPR[2]. In each RecognitionEquation the Assertion score must be normalized by dividing eachAssertion Term by the number of such terms in the equation. This resultsin producing a score of 100 and no score subtracted due to Negations andRPRs. This is basic to the capability of comparative scoring.

The negative terms may be any desired function which produces adesirable diminution of the score if some undesireable enclave ispresent in the shape. For REQ 0-0, for example, the score should bereduced if there are two enclaves in the shape which score high on aloop template. The best loop score is called BL, while the Second BestLoop is called SBL. In order to keep scores normalized, it is alsonecessary to have a negation function which does not add to the REQscore if the unwanted enclave is absent. Another characteristic of agood Negation function is that it should not subtract too much; if 100points were subtracted in a case where SBL was 100, the resulting REQ0-0 score would be unable to compete in the very important doublesreject comparison. One of the useful functions is that shown in FIG. 12;the amount to be subtracted is zero so long as the argument is lowerthan 75, but becomes equal to 100 minus the argument for argument valuesgreater than 75.

MEMBERSHIP QUALIFICATION

A method of qualifying white pixels for membership in an enclave isillustrated in FIG. 13A. This shows a series of sketches illustratingprogressive phases of membership qualification in a clean character. Thephrase "clean character" means the image of an alpha-numeric characterwhich does not have voids or extra black pixels.

For purposes of the following explanations, each pixel is identified byits x-y coordinates relative to a predetermined point in the image; forFIG. 13A the test COR A is at location 0,0.

FIG. 13A shows four phases of the progressive qualifying activity. FIG.13A.1, (Phase 1) shows the choice of Pixel A as a Test Cor location, andit also shows three white pixels that have been qualified formembership; these pixels have been labeled "I". They qualified becausethey "touch" Pixel A. "Touching" is defined as being next pixelneighbors on the same row or same column. A further requirement forqualification is that the white column. A further requirement forqualification is that the white pixel must be bounded by a black pixelin the same row or column. This black pixel must be in the same quadrantas the candidate pixel and must be located a distance further from theTest COR than the candidate. Note that the pixel directly to the West ofPixel A is not qualified because it is not bounded in its quadrant. (Asnoted above, the boundedness evaluations can also be made in diagonaldirections).

FIG. 13A.2 (Phase 2) shows additional pixels having been qualified as aresult of "touching" already qualified pixels and being "bounded" intheir respective quadrants. Pixels on quadrant boundaries are defined tobe in both quadrants.

FIG. 13A.3 (Phase 3) shows a further progression of the qualification"wave", and the Final phase is shown in FIG. 13A.4.

While the black line elements in the four phases of FIG. 13A are shownas being only one black pixel wide, one of the important advantages ofthis invention is that the width of a line forming a character (andtherefore the number of black pixels in the width of a black lineelement) is irrelevant to the basic operation of identifying thecharacter which it represents.

FIG. 13B contains three sketches labeled (FIG. 13B.1, FIG. 13B.2, andFIG. 13B.3, FIG. 10B, is called "Membership Qualification with Voids(Salt) and Extra Black Pixels (Pepper)". FIG. 13B.1) "Penetration ofVoid" shows a single pixel qualifying at the location of the void. FIG.13B.2) "Limitation of Penetration Due to Black Inhibitions". Inhibitionsextending the influence of black pixels occur in human vision as well asin the Artificial Fovea. The rule illustrated is expressed by thefollowing statement: any black pixel at coordinates i,j inhibitsmembership of pixels located at i+1,j+1; i+2,l+1; i+1,j+2; i+2,j(2. Thecoordinate numbers are positive in the directions away from the TestCOR. the inhibited pixels of particular interest in FIG. 13B.1 and FIG.13B.2 are labeled with black dots. Note that the inhibited pixelsprevent further spread of qualified pixels in the particular image shownin FIG. 13B.2).

The human fovea pays a linear price for inhibiting penetration ofenclaves through voids. This also occurs in my Artificial Fovea, asshown in FIG. 13B.2 labeled "Loss of Membership Due to Pepper Noise".Note there are two pixels lost to membership due to Black Inhibition(plus the loss of the black pepper pixel itself).

It is important to realize that the "labeling" of pixels and the size ofenclaves is roughly proportional to the general influence of neighboringpixels in this invention, and not to some disasterous single accident ofnoise. Thus, it must be noted that the result of comparing the FinalPhase enclave of FIG. 13A1.4 to FIG. 13B.2 will produce a high degree ofcorrelation in spite of the void. This happy situation occurs more oftenfor my area based invention than it occurs for methods based on thecurvature of black pixel aggregates.

FIGS. 14 (A-E) and 15 (A,B) illustrate characters which will beincreasingly more difficult for a method based on "line tracking" tosuccessfully recognize the ones which are recognizable and reject theones which are not "safely" recognizable. Note in FIG. 14 the variationsin the width of the "line" which is easily handled by this invention butcreates difficult problems in prior art systems which use line thinningor skeletonization steps. The term "line tracking" is here intended tomean all those methods which attempt to measure the directionalcharacteristics of groups of black pixels. A classic example is Grenias.Another term for line tracking is "pattern contour tracking". There arean enormous number of examples of methods which expand upon thistechnique a number of which require "line thinning" or skeletonizationfor them to be operative.

The line tracking method is to be contrasted with the "saccadic flick"and "enclave measurement" techniques of this invention, as previouslydescribed. It cannot be emphasized too strongly that the measurementsbegin with the closure characteristics of each white pixel independentlyof other white pixels. The algomeration of white pixels into enclaves isperformed in a systematic way, completely independently of the slopecharacteristics of black pixel segments. The measurements of enclavestreat line segment voids ("salt noise") only in terms of how theyaffected the closure characteristics of of the primal white pixels.Irregularity of the edges of black line segments have only a minuteeffect on the scoring of enclaves. Black pixels which are separated fromthe main line segments ("pepper noise") affect the scoring primarily interms of how they change the closure characteristics of the primal whitepixels.

In a line tracking machine there may be extensive "pre-processing"performed on the pattern before tracking is attempted. The varioustransformations, which often include "filling" (change white pixels toblack), "smoothing", and "line thinning" are all conceptually part ofthe technique of line tracking. On patterns which are free of noise,these efforts are redundant and the line tracking machine generallyperforms well (for familiar shapes). On patterns which contain a smallamount of noise, the pre-processing generally helps. On patterns whichcontain a large amount of noise, the pre-processing and line trackingoften lead to that most heinous of errors: a substitution.

FIG. 14A shows the simplest possible case of a void. The image containsa single width black pixel line pattern. A completely unsophisticatedpre-processor/line-tracker would decide that the pattern has composed ofonly one loop instead of two. This invention will give a score of 100out of a possible 100 to each loop, and a Recognition Equation score of100 for an EIGHT. See FIG. 14D for details of the scoring. If the voidin the crossbar was wider, the score would decrease using thisinvention.

An obvious "improvement" to the pre-processor/line-tracker would be toautomatically fill in all single voids. This then will produce a correctdecision for FIG. 14A. This would lead the ever hopeful promoter of linetracking to demand of his engineer that the pattern of FIG. 14B be alsorecognized as an EIGHT. In order to do this, the engineer might add tothe pre-processor the following algorithm: if two and two only blackpixels are present in any three-by-three pixel matrix, then fill in theshortest path between them. This then would recognize the pattern ofFIG. 11B as an EIGHT. This invention will produce very low scores forLoops, Best in West, Best in East, etc., because the percentage of whitepixels bounded by black pixels is so low. See FIG. 14E for some detailsof the scoring using my invention. The outputs of all of the Recognitionequations will therefore also be very low, producing both absolute levelrejects as well as doubles rejects. This invention would call foranother sampling of the original pattern, using a different quantizinglevel or a larger sampling area, or both. This example may lead theunwary reader to assume that the pre-processing/line-tracking method issuperior. This is not so, because the pattern of FIG. 14C will berecognized as a satisfactory THREE by line tracking, thus producing theworst possible error: a substitution.

Referring now to the pattern of FIG. 14C, we observe it to be almostidentical to that of FIG. 14B. Only two black pixels have been moved,but the prior art void filling algorithm now used in the pre-processorproduces continuous lines everywhere except at the critical West Sidesof the loops. This invention will output a REJECT for this pattern, buta sophisticated line tracking machine may very well produce asubstitution. Engineers can continue to add special case exceptions(called AD HOC solutions by the profession) which fix special case butinvariably end up making matters much worse for cases that have not yetbeen tried.

The addition of "pepper noise" (extra black; pixels which are notconnected to line segments) compounds the problems facing the linetracking machine. FIGS. 15A and 15B show two illustrations of modestline thickening and pepper noise which will drive any line trackingmachine crazy. The problem is that too many line segments have beenstarted and went nowhere useful. Because even a few extra branches causean exponential rise in the number of permutations and combinations, theline tracking machine quickly runs out of ability to express theallowable pattern shapes. The problem is that a "computable" parameterdoes not exist for linetracking machines. Conversely, this inventionuses continuously computable parameters, rather than "decision tree"recognition logic. These continuously computable parameters are thescores that are continuously generated at every correlation step of theprocess. The patterns of FIG. 15A and 15B produce excellent scores forthe ZERO and the TWO using this invention.

The examples of FIG. 15A and 15B have been chosen to illustrate saltnoise in FIG. 15A and pepper noise in FIG. 15B, without combining thenoises. The difficulties which occur within a line tracking machine whenfaced with a combination of these noises can only be described as"awesome".

EXAMPLES OF DETAILED QUADRANT MEASUREMENTS

FIGS. 16A and 16B illustrate the basic working of the measurements whichprovide for recognition which is size independent. FIG. 16A "BIG ZERO"shows a character which is approximately 16 pixels high by 13 pixelswide. Utilizing the point "A" for the COR, all of the white pixels inthe NE quadrant are bounded in both the vertical and horizontaldirection, thus warranting the designation "s". There are 41 of thesepixels. (Note that white pixels on the vertical and horizontal axes ofthe COR are counted in both of the quadrants which they divide.) Thus"NE % s"=100%". FIG. 16B "SMALL ZERO" has only a total of 11 whitepixels in the NE quadrant of its analysis diagram. (Note again thatwhite pixels on the vertical and horizontal axes of the COR are countedin both of the quadrants which they divide.) Nevertheless its percentageof white pixels which are bounded both in the vertical and horizontaldirection is 100%; thus "NE %s=100%", the same as in the big zero. Inthe OCR Industry this is called "Size Normalization". Similarly the SE,SW, and NW quadrants show a similar analysis for the big and smallzeroes. Note that FIG. 16B is not an exactly scaled down version of FIG.16A. This was done on purpose to also illustrate the independence fromline noise.

FIGS. 16C "SMALL SEVEN" and FIG. 16D "BIG SEVEN" go further inillustrating size normalization. Both these figures have twosatisfactory enclaves. The COR for Enclave A is shown as Pixel A, whilethe COR for Enclave B is called Pixel B. (Note that the white pixelswhich separate the quadrants are scored in each quadrant, as previouslyexplained.) For Enclave A, the number of NE Members is 11, and "NE%s=100". SE Members=6, and "SE %s=100". In the SW quadrant there areonly three members, and these are directly below the COR. These membersare all bounded vertically only, and thus carry the designation of "v";this fact is expressed as "SW %v=100". In the NW quadrant there are 11members, and all of them are vertically bounded only; thus "NW %v=100".These four percentages are exactly the same as the percentages in theperfect Best-In-West template; thus the BIW feature score is equal to100. Enclave B, whose COR is at Pixel B, has its members bounded in avery different set of ways. Quadrant NE has s=100%. Quadrant SE has*=100%, since it has no white pixels bounded. Quadrant SW has 12 membersand % h=100. Quadrant NW has only 3 members, and all of them are boundedvertically and horizontally; %s is therefore 100 % for NW. The templatecalled "SEVEX" calls for exactly this set of relationships, and featureSEVEX has therefore a value of 100. The generic Recognition Equation REQ7-0 equals (BIW+SEVEX)/2 vNEG[SBIW]. Since BIW is 100, SEVEX is 100, andthere is no second best-in-west enclave, these score for REQ 7-0 is 100.

BIG SEVEN, shown in FIG. 16D illustrates size normalization and is to becompared with FIG. 16C. The figure has been deliberately drawn, however,to give a slightly different score in the NW quadrant. Because of theslight loop tendency in the NW, the NW %v is only 70%, while %s has 30%.The score for the BIW template is therefore reduced, becoming only 90instead of 100. This produces a REQ 7-0 score only only 95 instead of100.

The method of scoring a set of measurements against a known template isuniform throughout this specification. There are three terms in eachquadrant description of each template. These are %s, %v, %h. Theequation I use in scoring is as follows:

SCORE OF ENCLAVE E AGAINST TEMPLATE T

=(100-NE Absolute Difference)/4

+(100-SE Absolute Difference)/4

+(100-SW Absolute Difference)/4

+(100-NW Absolute Difference)/4

where each quadrant Absolute Difference

=absolute difference (%sE-v%sT)/3

+absolute difference (%hE-v%hT)/3

+absolute difference (%vE-v%vT)/3

FIG. 16E illustrates a "perfect" European SEVEN. This numeral, whoseRecognition Equation is designated REQ 7-1, requires four enclavesinstead of the two required for a normal SEVEN. The analysis is shown indetail and does not have any anomalies. Note that white pixels notclaimed as members by one enclave may well be claimed by another enclavewhose analysis was carried out later. (The order of analysis neveraffects the measurements or scoring. Similarly the temporary appellationattached to an enclave, such as Enclave A, or Enclave No. 2, neveraffects the measurements or scoring.)

The NW quadrant of Enclave C shows a case of "line-of-sight" inhibitionin qualifying white pixels as members of Enclave C; notice that thecrossbar of the SEVEN is between COR C and ten white pixels.Line-of-sight membership inhibition is a useful and important tool forthe prevention of unwanted "blooming" of enclave membership.

METHODS FOR HANDLING OVERLAPPED/TOUCHING CHARACTERS

Handprinting from uncontrolled sources contains great many patternswhich are overlapping-not-touching, touching-not-overlapping, andtouching-overlapping. In handwritten checks for example, charactersoften touch one another or a fraction line or touch each other and thefraction line, etc. This section describes some of the ways thisinvention can uniquely recognize such characters, although mosthandprint recognition algorithms are unable to cope with these defects.

One reason this invention is superior to most other methods is that itis not necessary to obtain precise segmentation. Since the scoring islinear and carries highly accurate measures of segmentation quality itis theoretically possible to perform measurements on all possiblesegmentations and then choose the best set of segmentations after theentire image has been analyzed in completely detail. In practice,however, it saves time and money to use a combination of measurementswhich can be used to generate a plot of the Probability of Segmentation(POS Plot) as a function of the horizontal axis. There are occurences,however, of handprinting in which two or more characters are so muchintertwined that the POS Plot is not useful. For these cases, thisinvention has the capability of using "Dyadic Recognition" and "TriadicRecognition. These concepts will be illustrated after discussing thebasic POS Plot.

Initially, when the control unit is presented with a new image, thefirst function performed is Segmentation Analysis. This ia an analysisof the entire image to find out whether more than one character occurswithin the image and, if so, where the probability of separation ishigh. In the case of Courtesy Amount Recognition (CAR) (the arabicnumerals entered onto a bank check) the image presented is the CourtesyAmount Field (CAF), and the CAF always contains more than one numeral.When numerals are separated by a vertical column of white pixels, thisfact is recorded together with the horizontal coordinate of thisoccurence. It is called a Vertical Column Clear Occurence (VCCO). Therecognition of this occurence is basic to any OCR scheme and methods foraccomplishing this are well known in the art represented in the patentliterature. FIG. 17A shows one example of this feature.

FIG. 17A also shows a more interesting occurence called anoverlapping-not-touching occurence (ONTO). The handprinted TWO and THREEare overlapping but not touching at both the top and bottom of thenumerals. An "ONTO Membership Pulse" is initiated at the point P in theArtificial Fovea (AF) during Segmentation Analysis. Point P may be thecenter of the AF so long as the black image is kept roughly centeredvertically as the image is shifted from right to left. FIG. 17B shows anExemplary ONTO Stage within the Artificial Fovea. A matrix of suchstages is simply added to the Artificial Fovea previously described.This new ONTO matrix within the Artificial Fovea is connected to themain AF only by the Pl,m input which inhibits an ONTO stage from firingif the Polyp at the same location represents a black pixel. If the Polyprepresents a white pixel, the ONTO stage at location, l,m is fired ifany of the eight surrounding ONTO stages have fired. The circuit to dothis is the eight input "OR GATE". The result of this logic is that akind of brushfire occurs in which each new ONTO member becomes thesource for a new brushfire. Continuous lines of black pixels will act as"firebreaks", but the brushfire will reach the top and bottom boundariesof the AF if there is any possible route. OR gate detectors (not shown)along the top and bottom boundaries fire when the brushfire reaches eachboundary. The horizontal extent of the ONTO matrix should not be asgreat as for the other functions of the AF, since it is a waste ofresources to try to detect segmentations in which the characters arewoven together but not touching for more than half of an averagecharacter width.

FIG. 17A shows a possible ONTO feature between the TWO and the THREE.The important route followed by the brushfire is indicated on thedrawing by a symbol composed of a dot inside a circle. Most of thebrushfire is not portrayed, in the interest of making the principle moreobvious.

The value of the ONTO feature will be recorded if both the top boundarydetector and the bottom boundary detector fire within a reasonably shorttime. Assuming the circuitry is asynchronous the length of time requiredfor the brushfire to finish its propagation should be less than amicrosecond. The probability of segmentation (POS) is higher the shorterthe length of time; the spread of firings is also significant, and thevalue of the ONTO feature will be a function of these measurements. Notethat the ONTO feature becomes a way of measuring the VCCO.

Another input to the POS Plot may be the values of what are called UpperSegmentation Templates (UST) and Lower Segmentation Templates (LST).FIG. 17C illustrates a number of pairs of numerals with upper and lowersegmentation enclaves indicated by down arrows and up arrows,respectively. FIG. 17D shows a detailed example of a lower segmentationenclave and its somewhat generalized templates. This invention makes useof the same fact that humans use if they have to perform segmentation,namely that there are enclaves and combinations of enclaves whichtypically occur between characters, even if they are touching. The mostuseful, for numerals is fact that most numerals do not have enclaveswhich are South opening or North opening. Therefore, if South and Northopening enclaves have high scores, there is a high probability of asegmentation point being close by. (Note that the open top FOUR is amajor exception to this rule--no matter, a high POS merely generates adetailed examination, and the linear scoring reveals the correctstructure.)

The example of FIG. 17D is instructive. It shows an enclave whose COR isat point A. A Lower Segmentation Template is also presented which callsfor the NE and NW measurements to have 100% O of the pixels bounded bothvertically and horizontally, while the SE and SW quadrants are to havezero % bounded both vertically and horizontally. Any deviation fromthese template specifications will result in a score of less than 100.When a Template is designed by a human operator, the Template may oftenbe improved by the human to the extent of specifying only thoseparameters which are required to produced the desired filtering action.Thus, in FIG. 17D, only the %s values are specified in the Template. Inevaluating the correlation scoring equation only the terms specified bythe Template are to be used. When no operator is present, this inventioncalls for machine learning of a Template which must have all theparameters listed. The learning capabilities of my machine are discussedin more detail later in this specification.

FIGS. 17E and 17F illustrate a totally different and novel way ofrecognizing characters which are touching/overlapping. This method is analternate to the POS Plot method, or it may be used to confirm amarginal acceptance. Both these figures contain a "dyad" of the same twotouching/overlapping characters, but the individual numeral shapes aredifferent and the positional relationship of the numerals is different.The basic method for recognizing such characters is called "MultipleCharacter Recognition". The first time this invention is presented withthis dyad a reject will occur. A human is called (either on-line orlater in an off-line mode). The human in the case shown identifies thedyad as a 3/6 double character and specifies that some or all of themeasured enclaves be made into templates. A new class of numerals iscreated called CLASS 36 and a new Recognition Equation is created.

A part of the Template correlation scoring is shown in FIG. 17F. One ofthe many possible methods of scoring the correlation between an EnclaveE shown in FIG. 17E and a Template T101 has been given in an earlierherein. It is fully normalized equation, in that its maximum value isalways 100 and its minimum value is always zero. The followingcomputations is presented in order to demonstrate the details of thisexemplary scoring technique. There are four terms to compute initially,one for each quadrant.

    ______________________________________                                        NE absolute difference                                                                           = (100 - 60)/3                                                                + (5 - 0)/3                                                                   + (35 - 0)/3                                                                  = 27                                                       SE absolute difference                                                                           = (100 - 100)/3                                                               + 0                                                                           + 0                                                                           = 0                                                        SW absolute difference                                                                           = (10 - 0)/8                                                                  + (0 - 0)/3                                                                   + (100 - 90)/3                                                                = 6                                                        M absolute difference                                                                            = (21 - 0)/3                                                                  + (0 - 0)/3                                                                   + (100 - 79)/3                                                                = 14                                                       score              = (100 - 27)/4                                                                + (100 - 0)/4                                                                 + (100 - 6)j4                                                                 r (100 - 14)/4                                                                = 84                                                       ______________________________________                                    

Note that pixels may have a different symbolic notation in differentquadrants; this occurs only along the quadrant boundaries. However, inthe preferred embodiment, no pixels are members of more than OneEnclave. In order to improve the ease of understanding, some of theenclaves in FIG. 16E do not have their pixel notation shown; these areshown instead in FIG. 16G. FIGS. 16F and 16H are also separated into twofigures for the same reason of clarity.

The analysis of Enclave E of FIG. 16E is routine. A Template T102 basedon this enclave has descriptors as follows:

NE %s=100

SE %h=100

SW %h=100

NW %s=100

Analysis of Enclave E of FIG. 16F (the set of these measurements ardesignated ME-14F) yields the same descriptors as T102, and thecorrelation of T102 versus ME-14F is therefore 100%.

FIG. 17G shows the pixel designations within Enclave B. (Note that therewould have been many more members of this enclave if this enclave hadbeen analyzed prior to the analysis of the enclave directly above it.) ATemplate T103 may be written as a result of choosing this dyad as theprototype of Class 36-0. Its descriptors are as follows:

NE %s=100

SE %s=100

SW %v=100

NW %v=100

Analysis of Enclave B of FIG. 17H shows exactly the same parameters. Thecorrelation of T103 versus MB-H is therefore 100%.

FIGS. 17G and 17H also show the pixel designations within Enclave C forthe two images. For Dyad 36-0 the north opening enclave (Enclave C) isfairly standard, since there are no voids. (Note however that fourpixels in the southwest area have been denied membership in the enclave.This is because of inhibition by black pixels closer to the Test COR.This has been discussed in the section describing membership rules.) ATemplate T104 may be written directly from the measurements. Itsdescriptors are:

NE %h=100

SE %s=100

SW %s=100

NW %h=100

Enclave C measurements from FIG. 17H are somewhat different. They are:

NE %h=100

SE %s=100

SW %v=1/4×100=25

SW %s=3/4×100=75

NW %h=100

To get the correlation score of MC-14H versus T104, first get thequadrant absolute differences. For quadrants NE, SE, and NW, thedifferences are zero.

    ______________________________________                                        SW absolute difference                                                                           = (100 - 75)/3                                                                + (25 - 0)/3                                                                  + (0 - 0)/3                                                                   = 33                                                       MC-14H vs. T104    = (100 - 0)/4                                                                 + (100 - 0)/4                                                                 + (100 - 33)/4                                                                + (100 - 0)/4                                                                 = 91                                                       ______________________________________                                    

A "bang-bang" recognition equation using specific template numbers asassertions, no negation terms, and no other qualifiers is easily writtenas follows:

    REQ 36-0=(T101+T102+T103+T104+T105+T106)/4

where each T term means the best correlation score yielded by thattemplate against any enclave of the image.

This equation yields a score of 100 for Dyad 36-0 and 96 for Dyad 36-1.This illustrates that one dyad can be used to recognize many. An evenbroader recognition equation can be written using assertion terms like"Best-In-West". The writing of "smarter" recognition equations will bediscussed under the heading of Learning Techniques.

An extension of the dyadic recognition method is the triadic method.This is again particularly useful for recognizing touching andoverlapping numerals. FIGS. 17I, 17J, and 17K illustrate an actual casewhich occurred during the computer based development of this invention.Three Examples of almost identical sets of numerals here submitted foranalysis. The first set consisted of the numerals ONE, ZERO, and NINEtouching, with no overlap. The image is shown in FIG. 17F. The analysiswas made and three significant enclaves were found. Their measurementsformed the basis for a Recognition Equation names REQ 109-Oi REQ109-0=(Best Loop+Next Best Loop+Best-In-South)/3-NEG (Best-In-West)

The image which is named Triad 109-0 scored 100 REQ 109-Oi The next bestscore was produced by REQ 0-1 which scored 71 points.

FIG. 17J shows a very closely related image in which the NINE overlapsthe ZERO considerably. This image was also analyzed by the invention andREQ 109-0 scored 97 points. The next best score was 85 points, producedby REQ 8-Oi.

FIG. 17K shows a related image in which the ONE overlaps the ZERO andthe NINE also overlaps the ZERO. This image was analyzed and REQ 109-0scored 97 points. The next best score was 84 points, again produced byREQ 8-0.

These high performance results are by no means unexpected in terms ofinformation theory. A great deal of information remains even thoughimages may be mangled in complex ways. In commercial practice arecognition equation for a triad such as the example just given willinclude references to many more than just three enclaves, and there willbe additional negations to prevent images containing super-sets to scorewell.

Note that in FIGS. 17J and 17K an additional loop has been formed by theoverlapping ZERO and NINE. This loop has been essentially ignored by thelinear scoring and directed vector technique used throughout. Althoughthe absolute number of pixels in an enclave has been reduced to anunimportant parameter for the most part, the size of an enclave relativeto other close enclaves is to be carried through and used for scoringand for negation purposes where necessary. This technique is the subjectof an important feature in this invention. The quadrant scoring breaksdown when the number of pixels is small, and it is therefore desirableto have higher resolution in the images used with than is required forconstrained image recognition. Enclave C of FIG. 17H is an example ofmarginal resolution. The invention tends to be self recovering, however,since marginal resolution typically produces rejects in this invention,and rejects automatically cause the machine to re-process the charactersusing a higher resolution.

ARTIFICIAL FOVEA AS A COMPUTER ELEMENT

FIG. 18 illustrates the use of an Artificial Fovea 309 as an element ina computer. Within the dotted line 300 are shown in block form the majorcomponents of a modern serial Van Neuman type computer. These componentsare a Backplane Bus 301, a Random Access Memory (RAM) 302, a Read OnlyMemory (ROM) 303, an Arithmetic Unit 304, a Peripheral Channel 305, aDisk Controller 306 (including magnetic disks and optical disks) and aTape Controller 307, (including various forms of magnetic tapetransports). Such a general purpose computer is often augmented byspecial purpose processors, such as a "Vector Processor 308 (examplesare the Vector Processors which are attached to the Cray and Control"Data "supercomputers"), and the Fast Fourier Transform Processor 310(offered commercially as a single card which plugs into the backplanebus). These special purpose processors typically are interfaced to thegeneral purpose computer 30D using any one of several standard"backplane bus" protocols such as the "MultiBus" and the "VM Bus." Theyare typically sent a relatively small amount of data and assigned ahighly complex set of operations to perform on this data. Communicationback and forth is almost invariably on an "interrupt" basis using thebus protocol.

An Artificial Fovea may also be used as a special purpose processor inconjunction with a general purpose computer. FIG. 18 shows a blockcontaining an Artificial Fovea "processor" in the same configuration.Thus an Artificial Fovea can be used in close conjunction with a generalpurpose computer and can be assigned jobs by programs written in a greatmany of the standard higher level languages such as FORTRAN, "C",PASCAL, ADA, etc. Special purpose compilers can also be written toutilize completely the peculiar capabilities of the Artificial Fovea.

PLURALITY OF ARTIFICIAL FOVEAE

FIG. 19 shows a block diagram of a parallel configuration showing aplurality of Artificial Foveae 401, 402, 403, 404, 40N figure isdesigned deliberately to be similar to FIG. 3A. Pattern Source 10' maybe any kind of a scanner or image lift which can accept pictorialinformation from a check deposit slip or other document and outputelectrical signal which are responsive to the pictorial information.These electrical signals are distributed to any one of the fiveArtificial Foveae shown by a control block (not shown). The criterionfor deciding to which AF 401 . . . 40N to send the pattern is simplywhether the AF is busy or not. The plurality of AF send their outputs tothe Recognition Scoring block 410 and thence to the Utilization Device411.

The normal reason for having a plurality of foveae in the system is thatthe complexity of the electronic functions to be performed in thisinvention is so great as to make each Artificial Fovea almost invariablyslower in the completion of a unit task than the functions surroundingit. Thus, an image lift consisting of a paper transport and a column ofphotocells may very easily scan five hundred alpha numeric charactersper second, while a first generation Artificial Fovea may be only ableto analyze 100 per second. Thus five AF are needed to keep up with theinput. The same situation applies to the Recognition Scoring block 410.Recognition Scoring is much simpler and more straightforward than theArtificial Fovea and a five-to-one ratio may also be appropriate forthese functions.

RESOLUTION MODIFICATION

FIG. 20A is a block diagram of a partial Recognition Machine 500 showinga Resolution Modification component 501. Other elements also shown arethe Image Lifting 502, the Quantizing 503, the Artificial Fovea 504, theRecognition Scoring 505, and the Controller 506 units.

The object of the Resolution Modification component is to modify thenumber of pixels contained in a particular image so that the image maybe recognized speedily and with the least cost possible. Since thelength of time necessary for analysis goes up exponentially as afunction of the number of pixels in a character, the ResolutionModification element initially reduces the resolution as much aspossible, consistent with its recent experience with earlier images.

FIG. 20B shows an exemplary original full size scan, with gray scaleshown as hexadecimal symbol. In the example shown, the original imagewas scanned with a set of photocells which generated an analog output.The analog output was converted to a sixteen level digitalrepresentation. Conventionally, the 16 levels are printed by using thecodes 0 to 9, with A=10, B 11, C =12, D=13, E=14, and F=15. Again byconvention, a pixel label F is the blackest pixel. These symbolsrepresent the original conversion which is typically made withinmicroseconds of each analog read-out. Many research projects use 256levels of gray scale for the initial conversion, but 16 levels issatisfactory to illustrate the theory. The term "quantization" isreserved in this discussion for the binary choice which decides whethera pixel is to be considered black or white.

Continuing now with FIG. 20B, let us assume that three resolutions areavailable: they are a reduction of three in the x direction and 3 in they direction, or two in x and two in y, and no reduction at all. Let usdesignate these reductions as 3×3, 2×2, and 1×1. FIG. 20C shows ablackwhite quantization at the 1×1 resolution level. There are manymethods of determining the grayness of a pixel which should be optimallybe called black; only one of them will be discussed here. One of thesimplest is to add up all the gray scale values of the pixels in aparticular image and divide by the number of pixels. The resultingquotient is the blackwhite quantizing level.

The reason for this discussion of resolution modification is todemonstrate that some images cannot be analyzed properly using a reducedresolution. The image of FIG. 20C is such an example. It should be clearto the reader that no "Test COR" can be found that will generatemeasurements that will correlate well with a "Best Loop" Template. Themachinery shown in FIG. 20A will generate a Reject in this case and theController block will trigger the Resolution Modification block togenerate a new image at a higher resolution and send that image downlinefor analysis. Since the only higher resolution available in this exampleis 1×1, the machinery will quantize each pixel of FIG. 20B independentlyof its neighbors. The result is shown in FIG. 20D.

The reader should be able to observe that a number of good "Test CORS"points are possible in the image shown in FIG. 20D, and therefore highcorrelation is possible with the "Best Loop" Template, and an acceptablescore for the numeral "ZERO" will be obtained.

LEARNING CAPABILITIES

FIG. 21A shows a block diagram of a machine employing a Learning Module.This discussion will primarily deal with the methods by which a humanteacher can be efficiently employed to help this invention to learn fromexperience. However, this does not preclude the later description of amachine which can learn from experience without the use of a teacher.

The machine of FIG. 21A includes most of the functions that have beenpreviously discussed, such as Image Lifting, Quantizing, ResolutionModification, Segmentation Plotting, Artificial Fovea, RecognitionScoring, and Reject Scoring.

The simplest and most direct way for learning to occur is by having ahuman operator on line with the SF reader. The operation of thissimplest mode is as follows: when a reject occurs the help of theoperator is solicited by the machine by flashing the image of theunrecognized data on a screen in front of the operator. The operatorhits a key on the keyboard indicating to the machine what symbol orsymbols should be assigned to the image. This information enables thereader to continue with its processing, including the printing ofMagnetic Ink Characters on the bottom of the check. Thus far, the stepsare well known and are utilized in the present state-of-the-art.

The new steps are added by the Learning Module of FIG. 21A. Briefly,these steps include adding one or more new Templates if necessary andnew Recognition Equations if necessary. These new features will allowthe machine to recognize the image automatically the next time it or asimilar image appears. Moreover, during this learning step or phase, thecheck writer's unique character formation can be stored in the machinefor later authentication and/or validation of a check to thereby avoidforgeries.

FIG. 21B shows the first simple illustrative example. Let us assume thatone of our clients characteristically writes his zeroes with a largevoid on the right side. The Recognition Equation for generic zeroesrequires a high score on the Best Loop template. Such a high score wouldnormally be generated using a COR located approximately at Pixel A inFIG. 21B. Due to the large void in the NE quadrant, the Best Looptemplate produces a score of less than 80, and REQ O-O likewise producesa score less than the reject level. In fact, no Recognition Equationscan produce an acceptable level output. The image is rejected, and theimage comes on line. The operator indicates that the machine should"learn" the new shape. The Learning Module sends the image back throughthe Artificial Fovea again, but requires more detailed reporting. Itdetermines that, if a COR located at Pixel A' is used, a high outputfrom the BEST-IN-EAST template is generated. If this output is highenough, no new templates need to be learned. All that is necessary isfor the Learning Module to generate a Recognition Equation for a newsub-class of zeroes, called REQ O-1. This equation will consist of theterm BEST-IN-EAST, and several negations to prevent its output frombeing high for the numerals TWO, FIVE, and others containing enclavesopening to the East.

A Recognition Equation, (somewhat simplified) for this east opening zerois as follows:

    REQ 0-1=BIE-NEG[BIW]-NEG[SBL]

See FIG. 12 and related discussion for a review of these terms ifnecessary. Note that a first Best Loop (BL) is not negated, because agood score on BL is still quite likely; note also, however, that a highscore on a Second Best Loop (SBL) must be negated because the image maybe an EIGHT with an eastern void on the top loop.

In addition to negations, it may be necessary to add other terms whichdescribe the relative positions required of the various enclaves. Thesingle enclave case shown in FIG. 21A has been deliberately picked to bean initial simple introduction to operational learning problems whosesolutions require complex computations and much like.

The operator/teacher will be occasionally asked to make difficultdecisions. These difficult decisions fall into several categories.First, if the rejected image is so bad that the normal amount field (notthe Courtesy Amount Field) must be consulted, the operator shouldprobably not attempt to teach the SF machine the meaning of thedistorted image. Second, the image may seen readable enough to theoperator/teacher but it may still be in conflict with some characterunknown to the operator/teacher. For example, if the alphanumericcharacter "c" had been added to the SF machine's list of recognizablecharacters, the image of FIG. 21B would be clearly a dangerous shape tobe called a "ZERO". Such hidden conflicts must be discovered andresolved before any new shapes are accepted into the operationalrecognition set. If the operator/teacher is also a researcher skilled inthe SF art, then it may be possible to make such a quick decision. Whatis really necessary, however, is allow the Learning Module to conduct anearly exhaustive conflict check using a great many images having someenclaves common to the new candidate. This conflict check will, ingeneral, take so long to perform that it cannot be performed "on-line"while the reading of other bank checks is delayed. Thus an economicallyviable machine will likely have the reject corrected for the tally tape,but the image and correct symbol will be saved for off-line learning.Such off-line learning is called "Dream Learning".

The shape shown in FIG. 21B has illustrated a condition in which theteaching process must provide a new Recognition Equation but does nothave to provide any new Templates, since at least one high scoringtemplate already existed for the enclave in question. In the earlymonths of operation in a new writing environment many enclaves will befound that do not score well on the Templates which were installed atthe factory. An example of this may be drawn from FIGS. 17E and 17F. thesouth-opening Enclave, whose COR is labeled "E" is an Enclave whosemeasurements would not typically be installed at the factory. In orderto write a useful Recognition Equation for the Dyads shown, it would benecessary to teach the machine a Template whose correlation with similarmeasurements would produce a high score. The operator/teacher wouldobserve that Enclaves A, B, C, and D produced good scores on alreadyrecorded templates, but Enclave E did not. There is a manual way and anautomatic way to select the best COR for this new template. The manualway is to have the operator/teacher call up a program within theLearning Module which allows the operator/teacher to set a COR locationinto the machine using a "mouse", or "joystick", or by using keyboardcoordinate entry. The operator/teacher should know that the mostdistinctive scores for a three-sided enclave are generated when the CORis placed on the edge of the open side, near the middle. The automaticway is to have the program generate the measurements for all thepossible COR locations with the Enclave and then pick the location whichproduces the most "useful" measurements for a new template.

The definition of "useful" must now be discussed. If the enclave beingmeasured is fairly similar to many already stored, but just happens tohave a little quirk which causes it to score poorly on the standardtemplates, then the definition of "useful" should be to write a newTemplate which can be added to an existing class of templates; in thiscase that class of templates is the South-opening "Best-In-South" class.In this case, the criterion should be to choose the COR which correlatesbest with other templates already members of that class, while at thesame time correlating the worst with templates which are members ofother classes. In other words, the new template should have somegenerality unless the enclave is an absolute "oddball".

The case of the absolute oddball is more easily dealt with. The best CORwill be the location which produces measurements which are the mostdifferent from any templates already stored. The new template should beassigned a template number which is not part of a recognized class oftemplates, and a new Recognition Equation can be written automaticallywhich calls for that specific template number (in addition to otherterms in the equation).

In the case of the enclave which is made part of an existing class oftemplates, the existing Recognition Equation will be satisfactory, and anew equation should not be written.

It may be recognized that careless addition of new templates may causeconflicts in the same way that new Recognition Equations may do. Newtemplates must also be checked against a known image data base atlength, preferably during "dream learning".

CAPABILITIES FOR REJECTING NONSENSE SHAPES AND DISORDERLY NOISE

This invention has two intrinsic levels of capability for rejectingshapes which have no meaning and images which contain only random ornear-random noise. The first level is at the Template scoring level, andthe second is at the Recognition Equation level.

Consider the image shown in FIG. 22A. This is intended to represent anonsense shape which scores poorly at the Template level. if the machinehas been taught only Templates derived from the generic enclaves shownin FIG. 10A then every Template Cross Enclave Score (TXES) will be lowfor all possible COR locations. Since criteria for accepting a Best TXEScan be made dependent upon absolute level and distance from the nextbest TXES, the machine can be adjusted to actually ignore shapes beforeeven computing the Recognition Equation Score (RESC).

Note, however, that situations in which a shape can be ignored solely onthe basis of poor TXE scores will tend to be limited to images whichhave mostly short disconnected sections of black pixels. Thus, random ornearly random noise will be often skipped over quite quickly by amachine well-constructed from the ideas of this invention. When thefragmented lines are not random, however, as is shown in FIGS. 11B and11C, fairly good TXE scores occur and the images may be satisfactorilyrecognized.

A more interesting case is illustrated in FIG. 22B, which shows anonsense shape which scores richly at the TXE level. Good scores wouldbe developed for Best Loop (BL), Second Best Loop (SBL), Best-In-East(BIE), Best-In-West (BIW), and the four square corners. If this DOLLARSIGN ($) was part of the desired set of characters to be recognized, aRecognition Equation (REQ) would exist and would score highly. If on theother hand, the DOLLAR SIGN was not intended to be recognized, the REQwould not exist; REQs for shapes which are sub-sets of "$" must havenegation terms included or else conflicts will occur. Examples of suchsub-sets are the TWO, the FIVE, and the EIGHT. If all the sub-set REQsare properly negated, the DOLLAR SIGN can be ignored and treated as ifit didn't exist.

In any structured application to which my invention may be put, however,the system will be much improved by carrying the reject logic one stagefurther. In a banking application where the Courtesy Amount Field (CAF)is to be recognized, this third level of reject control compriseslogical statements which will consider the location of control symbols,rectangular boxes, lines, decimal points, cents indicators (discussedabove in connection with FIGS. 1-2), and nonsense images. For example, areally high confidence CAF should consist of a recognized rectangularbox surrounding a"" heading a string of high scoring numerics, followsby a DECIMAL POINT, followed by some form of fractional dollar symbol.As shown in FIGS. 2-1 through 2-12, many combinations of these elementsexist in today's acceptable handprinted CAFs, and the Field AcceptanceLogic must be able to handle these variations. It must also be able torecognize that a poorly scoring nonsense shape may be proper cause toreject the whole CAF in one case, whereas in a different case a garbageshape may be allowed.

SPECIAL MEASUREMENT FOR CLOSED TOP FOUR

In the field of Optical Character Recognition, just as in the betterknown fields of Physics and Philosophy, no single all-encompassingformula has been found which can be used to solve all problems. It isthe mark of a really good Philosophy that it provides a matrix in whichunusual methods can be nurtured and exercised.

Such an unusual method is required to help distinguish between theclosed top FOUR and the normal NINE. Because the most important newmeasurements of this invention are primarily used to "generalize" ornormalize the differences between handwritten topological features,these new enclave measurements must be supplemented by SpecialMeasurements when the topology of two classes is too similar. The humanFovea has an enormous number of measurements which are not primarilybased on topology. The "straightness" of a line is one major example.

In separating with high confidence an arbitrary example of a closed topFOUR from a normal NINE, this invention uses as many topologicalfeatures as it can. FIG. 10B illustrates the use of the NE, SE, and SWSquare Corners, plus the NW Triangular Corner and the Best Loop. Ofthese features, only the NE Square Corner and the SE Square Corner arereliably different. The Best Loop is invariably the strongest featurepresent, however, and the human fovea almost certainly measures moredetails about the shape of the Best Loop. One of the virtues of thisinvention is that it makes possible accurate assessments of the shapesof selected enclaves as well as their topology.

A method called the "Pointyness Triangle" (PT Method) will be explainedto illustrate the versatility of my invention. The PT Method starts withthe coordinates of the COR from which the BL feature was measured. Threepoints are then established. The first one is called Pne. It is thefurther point from the COR within the NE quadrant. The distancemeasurement is computed using the sum of the squares. The second point,called Psw is located by finding the position of the enclave memberwhich is furthest away from the COR in the SW quadrant. The third point,called Pse, is similarly located in the SE quadrant. The lines areconnected between the three points and they are called the PointynessTriangle. The Pointyness Ratio is the number of members within theenclave as a whole divided by the number of members within the triangle.For FIG. 23A, the Pointyness Ratio is unity.

FIG. 23B shows the Pointyness Triangle superimposed on a normal wellformed NINE. The Pointyness Ratio is approximately 2.5. Decisions as towhich pixels are inside or outside can be made pretty much at the whimof the machine designed; this can be done by using equations andintegral arithmetic, or it can be done by table lookup. The importantthing is to get the Pointyness Ratio into the Recognition Equations forthe closed top FOUR and the NINE in such a linear way that a numeralscoring on the borderline between the two classes can be gracefullyrejected.

The technique used in successfully demonstrating this feature was tocreate two features used for negation only; these features are calledtri[4] and tri[9]. They are clipped and off-set functions of thePointness Ratios, where the clipping and the off-set values areparameters that can be varied according to learning performance.Referring to FIG. 11F, the Recognition Equation uses a feature calledBest Sharp Loop (BSL). We now define BSL as equal to BL-tri[4].Similarly, FIG. 8K uses a feature called Best Round Loop (BRL). We nowdefine BRL as equal to BL-tri[9].

SPECIAL MEASUREMENTS FOR PERIODS AND UNRESOLVED BLOBS

There are some types of images and defective images which might seem tobe difficult or impossible to recognize using the encalve measurementstechnique. The PERIOD (".") is an example of this derived from the OCRindustry, since it normally has no interior white pixels. NINES, EIGHTSand SIXES are examples of numerals which often have significant loopsfilled in due to carelessness or the use of too wide a writinginstrument.

Contrary to expectation, these images provide some of the most novel andprovocative examples of the Saccadic Flick and Artificial Fovea.

FIG. 24A shows a perfectly round PERIOD using the conventionalBlack/White display. While it is true that four triangular corner typeenclaves are present, these are pretty small with respect to the area ofthe character.

A much more interesting solution is to invert the color of the pixels;the character will then appear as in FIG. 24B, and a high quality BestLoop enclave can be measured using the methods previously taught by thisinvention. In order to separate the PERIOD class from the BLOB-ZEROclass, a term which compares blob sizes can be used, in addition tocontextual information. The most significant use of the PERIOD is as aDECIMAL POINT in the Courtesy Amount Field of checks. BLOB ZEROssometimes occur in the cents section of the amount, since that sectionis often written smaller and with less care than the dollar amount.

FIG. 24C illustrates an EIGHT with a blob lower loop. This condition isfairly characteristic of right handed people in a hurry. The lower loopbecomes slanted and thin and narrow enough so that few or no whitepixels can be resolved. The upper loop often has a void in the NW, and adistinctive Template, not included in the north opening feature class orthe east opening feature class, should be taught to the machine. Theresulting REQ should contain at least the following terms:

    REQ BLOB EIGHT=(INVERTED BL+T[NW]+BIW)/3

Significant extensions can be made of this "color flip" technique awayfrom the world of black/white and into the world of gray scale images.Such images are most prevalent in so-called "scene analysis" and"bin-picking". In these worlds the information in the images cannoteasily be displayed using only two levels of intensity, as is done inOCR. In the "bin-picking" application (bin-picking is the automaticselection of a single part out of a bin containing many diverse shapesstrewn in random placing) significant features may often be discoveredby checking the images for enclaves which occur only within certain grayscale "windows". For example, a bowl may be illuminated in such a waythat the center of the bowl and the rim show spectacular reflection,while being connected by means of areas which can be recognized byselecting only those pixels having an intermediate intensity.

SPECIAL MEASUREMENTS USING ABSOLUTE AND RELATIVE SIZES OF ENCLAVES

FIG. 25A, however, a shape is illustrated which may cause some conflictbetween the ZERO class and the EIGHT class since it has a Best Loop anda Second Best Loop and a potential Best-In-West arising from the dimpleon the left of the main loop. My invention provides methods for treatingsuch shapes in very much the same way that humans probably do. First, noCOR can be found in the dimple that produces four good quadrants;secondly, if a marginal sized enclave is found, it can be compared tothe sizes of other enclaves associated with the image and eitherentirely ignored, or may be used as a "spoiler" to preventsubstitutions.

FIG. 25B is an example of a complex function derived from relativeenclave sizes. This particular function is zero when the ratio is lessthan 15%, and zero again when the ratio is between 65 and 100%. Inbetween, the TLLL Function peaks at 30%. Such a function is useful as anAssertion term in a Recognition Equation particularly designed forrecognizing this shape, and it may also be used as a Negation term inother REQs.

METHODS FOR RECOGNIZING FRACTIONS AND NON-NUMERIC SYMBOLS

FIG. 26A shows a typical handprinted way of expressing the fractionalpart of a dollar within the CAF, as used in banking. The example is atouching ZERO-SIX in the numerator, with an almost horizontal lineunderneath. This horizontal or fraction line represents the mathematicalsymbol for division. Below the division symbol is the numeral triadONE-ZERO-ZERO, all numerals touching. This represents the denominator inthe fraction. In handprinted checks, the numerals in the denominator, ifpresent, are always of value 100. An XX symbol sometimes does appear asthe denominator.

It is very important to locate these fractions early in the recognitionprocess for CAFs. One reason for locating them early is that they onlyoccur in the CAF and at the end of the cursively written or spelledamount field. Thus, the location of two examples of the same fraction onthe same check can be used as a powerful method to locate the CAF. Thetwo fractions can also be used against each other to improve theprobability of correct recognition.

An excellent time to recognize these fractions is during the early sweepwhich has been generating "Probability of Segmentation". this wasdiscussed under the heading of Handling Overlapped Touching Characters.

Because the fastest and easiest features to recognize are lines that runexactly on horizontal and vertical axes, the first features to bediscovered will be portions of the pre-printed box, if that box existson the check. Unless the check has been bouncing during the scan,continuous horizontal blacks (exhibiting close to the minimumreflectance values) will be found which are very close to exactlyhorizontal and continue for a distance which is more than half an inchlong. If the scanning resolution is poor, the reflectance values sampledmay be poor due to straddling a line. To improve the reliability of suchmeasurements horizontal "masks" which add up the grey scale reflectancevalues in selected X-Y locations have been successfully used in thepractice of the invention. These masks will be found to be particularlyuseful in locating the division symbol, which tends to have greaterreflectance than a pre-printed line and is not likely to fall exactly onthe horizontal axis of scanning.

APPLICATION OF INVENTION TO THE RECOGNITION OF TYPEWRITTEN OR CHARACTERS

In general, the methods described for reading handprinted numericcharacters and symbols apply directly to the recognition of typewrittenor printed characters. The main difference is that the recognition rateshould be much higher for the machine printed characters. The followinggeneralizations may also be made for machine printed numerals in bankingsituations as opposed to handprinted numerals:

1) The variations in height and width will be much less;

2) The contrast will be greater;

3) The percentage of voids and extraneous black pixels will be smaller.

4) The percentage of unusually shaped enclaves will be smaller.

FIG. 27 shows some examples of a quantized set of arabic numeralsoriginally printed by a typewriter. The examples show that the numeralsmay be easily separated and that the recognition principles of areeasily applied.

FIG. 28 shows some font drawings from the standardized set OCR-A, andFIG. 29 shows some font drawings from a less stylized set called OCR-B.Both OCR-A and B were designed for the specific purpose of producingfonts which was easily recognizable by optical reading machines.

It will improve the performance of the reading machine to recognize thattypewritten characters are being recognized as opposed to handprintedcharacters. The following parameters measured for this purpose:

1) Height and width of characters,

2) Uniformity of spacing and pitch,

3) Uniformity of line thickness,

4) Variance of centerlines of the group of characters for straight line,

5) Uniformity of characters belonging to the same class,

6) Conformance to known and stored parameters for hundred standard fontstyles.

READING OF CHARACTERS PRODUCED BY DOT MATRIX PRINTERS

FIG. 30 shows a drawing of the numeral TWO as it might appear ifproduced by a typical lead slug impression or any one of a number ofprinting methods which utilize a raised metal area in the shape of thedesired character.

The AMerican National Standard Matrix Character Sets for OpticalCharacter Recognition shows an impression which can be created byselectively blackening points in a 5"×7" matrix. Such a matrix is calleda "Dot Matrix".

There are several desireable features of such a printer. Firstly, thesame mechanism may be used to print characters from almost any font,including graphics. Secondly, the ragged appearance of a coarse matrixis compensated for by the high printing speed obtained. Thirdly, bytaking more time and printing dots in a higher resolution matrix, theappearance can be improved to the point where it can hardly bedistinguished from "typewriter" quality. Fourthly, the production costof such a printer is low relative to almost all other methods suitablefor computer output.

From the point of view of machine reading of the output of dot matrixprinters, however, a coarse matrix can raise havoc with the performanceof OCR machines based on classical methods such as Mask Matching andStroke Analysis. The primary problem is that while the human eye iswilling to integrate the almost disconnected dots into continuous lines,the optical scanner cannot do this. Notice that a Nyquist sampling(sampling at spacings equal to 0.7 of the minimum line thickness) of thecharacter shown in FIG. 30 will produce an excellent character image forany o the classical methods. A similar scanning of FIG. 31, however,will rarely yield a high quality digital image because of the circularand discrete nature of the dots. If the vertical and horizontal samplingcould be made to be coincident with the printing matrix, the digitalimage would be satisfactory, but this is impossible to obtain inpractical scanners.

The result of scanning coarse dot matrix impressions invariably producesdigital images which have considerable degradation, primarily voids. Ifthe quantizing level is changed to compensate, important gaps will startto close.

Since my invention does not require black pixels to be on specificmatrix points, or even that black lines exhibit some specified degree ofcontinuity, the invention disclosed in this specification will produce ahigher recognition rate and a lower substitution rate for dot matrixcharacters than any other previous method. These rates may not be 100%and 0%, however, since a coarse matrix truly does degrade characters,even as interpreted by the human eye.

While there has been shown and described the preferred embodiments ofthe invention, it will be appreciated that numerous modifications andadaptations of the invention will be readily apparent to those skilledin the art. For example, in early forms of the invention, I usedcomputer software techniques to successfully perform substantially allof the functions disclosed herein. It is intended to encompass all suchmodifications and adaptations as may come within the spirit and scope ofthe claims appended hereto.

What is claimed is:
 1. Banking apparatus for high speed processing bankchecks, drafts and like documents having courtesy amount fields thereonwith numeric characters therein, comprising:means for conveying saiddocuments along a path, means for locating and scanning said courtesyamount field to produce an optical image thereof, digitizing means forconverting said optical image of the numeric character in said courtesyamount field to an electronic binary black/white pixel image thereof, aplurality of parallel connected artificial fovea for emulating thebehavior of a human fovea, each artificial fovea including means fortemporary storage of said pixel image, means for measuring sections ofsaid pixel images to produce image measurements, means for scoring saidimage measurements, and recognition scoring means having recognitionequations stored therein, said recognition scoring means being connectedto said plurality of artificial fovea to score the image measurementsfrom each said artificial fovea and identify the numeric character fromthe respective artificial fovea.
 2. The banking apparatus as defined inclaim 1 wherein said means for measuring in each said artificial foveaincludes means for examining each pixel in said black/white pixel imageto locate one or more centers of recognition pixels,means fordetermining the boundedness of other pixels having a specificrelationship to said center of recognition pixel, and producing saidimage measurements to recognize said numeric characters.
 3. The bankingapparatus defined in claim 1 including resolution modification meansconnected between said digitizing means and said plurality of artificialfovea to initially reduce the number of pixels contained in a particularimage and if one of said artificial fovea reject a pixel image of anumeric character with reduced resolution, said resolution modificationmeans increases the resolution.